introduction-scikit-learn.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "56d4c0f1",
   "metadata": {},
   "source": [
    "# Introduction to Scikit-Learn(sklearn)\n",
    "\n",
    "This notebook demonstrates some of the most useful functions of the beautiful Scikit-Learn library.\n",
    "\n",
    "What we're going to cover:\n",
    "\n",
    "0.An end-to-end Scikit-learn workflow\n",
    "1.Getting the data ready\n",
    "2.Choose the right estimator/algorithm and use it to make predictions on our data\n",
    "3.Fit the model /algorithm amd use it to make prediction on our data\n",
    "4.Evaluating a model\n",
    "5.Improve a model\n",
    "6.Save and load a tained model\n",
    "7.Putting it all togrther!\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "d49b234b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import sklearn\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "618715b9",
   "metadata": {},
   "source": [
    "## 0.An end-to-end SCikit-Learn workflow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "9a213687",
   "metadata": {},
   "outputs": [
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       "     age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  \\\n",
       "0     63    1   3       145   233    1        0      150      0      2.3   \n",
       "1     37    1   2       130   250    0        1      187      0      3.5   \n",
       "2     41    0   1       130   204    0        0      172      0      1.4   \n",
       "3     56    1   1       120   236    0        1      178      0      0.8   \n",
       "4     57    0   0       120   354    0        1      163      1      0.6   \n",
       "..   ...  ...  ..       ...   ...  ...      ...      ...    ...      ...   \n",
       "298   57    0   0       140   241    0        1      123      1      0.2   \n",
       "299   45    1   3       110   264    0        1      132      0      1.2   \n",
       "300   68    1   0       144   193    1        1      141      0      3.4   \n",
       "301   57    1   0       130   131    0        1      115      1      1.2   \n",
       "302   57    0   1       130   236    0        0      174      0      0.0   \n",
       "\n",
       "     slope  ca  thal  target  \n",
       "0        0   0     1       1  \n",
       "1        0   0     2       1  \n",
       "2        2   0     2       1  \n",
       "3        2   0     2       1  \n",
       "4        2   0     2       1  \n",
       "..     ...  ..   ...     ...  \n",
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       "\n",
       "[303 rows x 14 columns]"
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     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 1.Get the data ready\n",
    "import pandas as pd\n",
    "heart_disease=pd.read_csv(\"csv/11.2 heart-disease.csv \")\n",
    "heart_disease"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ad441aa1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create X(Features matrix)\n",
    "x=heart_disease.drop(\"target\",axis=1)\n",
    "\n",
    "# Create Y(labels)\n",
    "y=heart_disease[\"target\"]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "cb99e4d9",
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "7465d87b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'bootstrap': True,\n",
       " 'ccp_alpha': 0.0,\n",
       " 'class_weight': None,\n",
       " 'criterion': 'gini',\n",
       " 'max_depth': None,\n",
       " 'max_features': 'sqrt',\n",
       " 'max_leaf_nodes': None,\n",
       " 'max_samples': None,\n",
       " 'min_impurity_decrease': 0.0,\n",
       " 'min_samples_leaf': 1,\n",
       " 'min_samples_split': 2,\n",
       " 'min_weight_fraction_leaf': 0.0,\n",
       " 'n_estimators': 100,\n",
       " 'n_jobs': None,\n",
       " 'oob_score': False,\n",
       " 'random_state': None,\n",
       " 'verbose': 0,\n",
       " 'warm_start': False}"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#  2. Choose the right model and hyperparameters\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "clf=RandomForestClassifier(n_estimators=100)\n",
    "\n",
    "# We'll keep the default hyperparameters\n",
    "clf.get_params()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "95fe92f3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 3.Fit the model to the training  data\n",
    "from sklearn.model_selection import train_test_split \n",
    "\n",
    "x_train, x_test, y_train, y_test=train_test_split(x,y, test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "8641eb1c",
   "metadata": {},
   "outputs": [],
   "source": [
    "clf.fit(x_train,y_train);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "3d45f88b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make a prediction\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "c4b1a52f",
   "metadata": {},
   "outputs": [
    {
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       "      <td>110</td>\n",
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       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>61 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  \\\n",
       "8     52    1   2       172   199    1        1      162      0      0.5   \n",
       "2     41    0   1       130   204    0        0      172      0      1.4   \n",
       "15    50    0   2       120   219    0        1      158      0      1.6   \n",
       "57    45    1   0       115   260    0        0      185      0      0.0   \n",
       "191   58    1   0       128   216    0        0      131      1      2.2   \n",
       "..   ...  ...  ..       ...   ...  ...      ...      ...    ...      ...   \n",
       "101   59    1   3       178   270    0        0      145      0      4.2   \n",
       "26    59    1   2       150   212    1        1      157      0      1.6   \n",
       "175   40    1   0       110   167    0        0      114      1      2.0   \n",
       "14    58    0   3       150   283    1        0      162      0      1.0   \n",
       "62    52    1   3       118   186    0        0      190      0      0.0   \n",
       "\n",
       "     slope  ca  thal  \n",
       "8        2   0     3  \n",
       "2        2   0     2  \n",
       "15       1   0     2  \n",
       "57       2   0     2  \n",
       "191      1   3     3  \n",
       "..     ...  ..   ...  \n",
       "101      0   0     3  \n",
       "26       2   0     2  \n",
       "175      1   0     3  \n",
       "14       2   0     2  \n",
       "62       1   0     1  \n",
       "\n",
       "[61 rows x 13 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "e5ada413",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, 1, 0, 1, 1, 1, 1,\n",
       "       0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 1, 1,\n",
       "       0, 1, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 0, 1, 1], dtype=int64)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_preds=clf.predict(x_test)\n",
    "y_preds                    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "253fa60d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8      1\n",
       "2      1\n",
       "15     1\n",
       "57     1\n",
       "191    0\n",
       "      ..\n",
       "101    1\n",
       "26     1\n",
       "175    0\n",
       "14     1\n",
       "62     1\n",
       "Name: target, Length: 61, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "a8bc192a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Evaluate the model on thr training data and test data\n",
    "\n",
    "clf.score(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "4d93eae2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8360655737704918"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.score(x_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "bd726613",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.81      0.81      0.81        26\n",
      "           1       0.86      0.86      0.86        35\n",
      "\n",
      "    accuracy                           0.84        61\n",
      "   macro avg       0.83      0.83      0.83        61\n",
      "weighted avg       0.84      0.84      0.84        61\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report, confusion_matrix, accuracy_score\n",
    "\n",
    "print(classification_report(y_test,y_preds))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "a41e27de",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[21,  5],\n",
       "       [ 5, 30]], dtype=int64)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(y_test,y_preds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "b044bd86",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8360655737704918"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y_test,y_preds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "9d1d8b98",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Trying model with 10 estimators..\n",
      "Model accuracy on test set: 80.33%\n",
      "\n",
      "Trying model with 20 estimators..\n",
      "Model accuracy on test set: 86.89%\n",
      "\n",
      "Trying model with 30 estimators..\n",
      "Model accuracy on test set: 80.33%\n",
      "\n",
      "Trying model with 40 estimators..\n",
      "Model accuracy on test set: 83.61%\n",
      "\n",
      "Trying model with 50 estimators..\n",
      "Model accuracy on test set: 85.25%\n",
      "\n",
      "Trying model with 60 estimators..\n",
      "Model accuracy on test set: 83.61%\n",
      "\n",
      "Trying model with 70 estimators..\n",
      "Model accuracy on test set: 83.61%\n",
      "\n",
      "Trying model with 80 estimators..\n",
      "Model accuracy on test set: 81.97%\n",
      "\n",
      "Trying model with 90 estimators..\n",
      "Model accuracy on test set: 85.25%\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# 5. Improve a model\n",
    "# Try different amount of n_estimators\n",
    "\n",
    "np.random.seed(42)\n",
    "for i in range(10,100,10):\n",
    "    print(f\"Trying model with {i} estimators..\")\n",
    "    clf=RandomForestClassifier(n_estimators=i).fit(x_train, y_train)\n",
    "    print(f\"Model accuracy on test set: {clf.score(x_test, y_test) * 100:.2f}%\")\n",
    "    print(\"\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "e6346d3f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 6. Save a model and load it\n",
    "import pickle\n",
    "\n",
    "pickle.dump(clf, open(\"random_forest_model_1.pkl\",\"wb\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "070496ca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8524590163934426"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loaded_model=pickle.load(open(\"random_forest_model_1.pkl\",\"rb\"))\n",
    "loaded_model.score(x_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa326634",
   "metadata": {},
   "source": [
    "# 1.Getting our data ready to be used with machine learning\n",
    "\n",
    "There main things we have to do:\n",
    "   \n",
    "   1.Split the data into feature and labels (usually `x` & `y`)\n",
    "   2.Filling (also called imputing) or disregarding missing values\n",
    "   3.Converting no-numerical values to numerical values (also calles feature encoding)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "92694da8",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "      <td>163</td>\n",
       "      <td>1</td>\n",
       "      <td>0.6</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  slope  \\\n",
       "0   63    1   3       145   233    1        0      150      0      2.3      0   \n",
       "1   37    1   2       130   250    0        1      187      0      3.5      0   \n",
       "2   41    0   1       130   204    0        0      172      0      1.4      2   \n",
       "3   56    1   1       120   236    0        1      178      0      0.8      2   \n",
       "4   57    0   0       120   354    0        1      163      1      0.6      2   \n",
       "\n",
       "   ca  thal  target  \n",
       "0   0     1       1  \n",
       "1   0     2       1  \n",
       "2   0     2       1  \n",
       "3   0     2       1  \n",
       "4   0     2       1  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "heart_disease.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "d038f44e",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th>fbs</th>\n",
       "      <th>restecg</th>\n",
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      ],
      "text/plain": [
       "   age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  slope  \\\n",
       "0   63    1   3       145   233    1        0      150      0      2.3      0   \n",
       "1   37    1   2       130   250    0        1      187      0      3.5      0   \n",
       "2   41    0   1       130   204    0        0      172      0      1.4      2   \n",
       "3   56    1   1       120   236    0        1      178      0      0.8      2   \n",
       "4   57    0   0       120   354    0        1      163      1      0.6      2   \n",
       "\n",
       "   ca  thal  \n",
       "0   0     1  \n",
       "1   0     2  \n",
       "2   0     2  \n",
       "3   0     2  \n",
       "4   0     2  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X=heart_disease.drop(\"target\" , axis=1 )\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "553dfe5e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1\n",
       "1    1\n",
       "2    1\n",
       "3    1\n",
       "4    1\n",
       "Name: target, dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y=heart_disease[\"target\"]\n",
    "Y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "945cc90c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split the data into training and test sets\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, Y_train, Y_test=train_test_split(X, Y, test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "ed923237",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((242, 13), (61, 13), (242,), (61,))"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape, X_test.shape, Y_train.shape, Y_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "a0547b1b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "242.4"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape[0] * 0.8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "ea4c59b7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "303"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(heart_disease)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "5731c933",
   "metadata": {},
   "outputs": [],
   "source": [
    "car_sales=pd.read_csv(\"csv/7.1 car-sales.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "d54cbfbd",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Make</th>\n",
       "      <th>Colour</th>\n",
       "      <th>Odometer (KM)</th>\n",
       "      <th>Doors</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>White</td>\n",
       "      <td>150043</td>\n",
       "      <td>4</td>\n",
       "      <td>$4,000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Honda</td>\n",
       "      <td>Red</td>\n",
       "      <td>87899</td>\n",
       "      <td>4</td>\n",
       "      <td>$5,000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>Blue</td>\n",
       "      <td>32549</td>\n",
       "      <td>3</td>\n",
       "      <td>$7,000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>BMW</td>\n",
       "      <td>Black</td>\n",
       "      <td>11179</td>\n",
       "      <td>5</td>\n",
       "      <td>$22,000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Nissan</td>\n",
       "      <td>White</td>\n",
       "      <td>213095</td>\n",
       "      <td>4</td>\n",
       "      <td>$3,500.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>Green</td>\n",
       "      <td>99213</td>\n",
       "      <td>4</td>\n",
       "      <td>$4,500.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Honda</td>\n",
       "      <td>Blue</td>\n",
       "      <td>45698</td>\n",
       "      <td>4</td>\n",
       "      <td>$7,500.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Honda</td>\n",
       "      <td>Blue</td>\n",
       "      <td>54738</td>\n",
       "      <td>4</td>\n",
       "      <td>$7,000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>White</td>\n",
       "      <td>60000</td>\n",
       "      <td>4</td>\n",
       "      <td>$6,250.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Nissan</td>\n",
       "      <td>White</td>\n",
       "      <td>31600</td>\n",
       "      <td>4</td>\n",
       "      <td>$9,700.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Make Colour  Odometer (KM)  Doors       Price\n",
       "0  Toyota  White         150043      4   $4,000.00\n",
       "1   Honda    Red          87899      4   $5,000.00\n",
       "2  Toyota   Blue          32549      3   $7,000.00\n",
       "3     BMW  Black          11179      5  $22,000.00\n",
       "4  Nissan  White         213095      4   $3,500.00\n",
       "5  Toyota  Green          99213      4   $4,500.00\n",
       "6   Honda   Blue          45698      4   $7,500.00\n",
       "7   Honda   Blue          54738      4   $7,000.00\n",
       "8  Toyota  White          60000      4   $6,250.00\n",
       "9  Nissan  White          31600      4   $9,700.00"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_sales"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "e9449f39",
   "metadata": {},
   "outputs": [],
   "source": [
    "x=car_sales.drop(\"Price\",axis=1)\n",
    "y=car_sales[\"Price\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "432199b0",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Split the data into training and test data\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "x_train, x_test, y_train, y_test=train_test_split(x, y, test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "3e8dcd0b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(8, 4)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "56ed0709",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2, 4)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "cf34f6df",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(8,)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "60ef5bbb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2,)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3714b442",
   "metadata": {},
   "source": [
    "## 1.1 Make sure it's all numerical"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "910144e4",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "car_sales=pd.read_csv(\"csv/car-sales-extended.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "2f7dcd0c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Make</th>\n",
       "      <th>Colour</th>\n",
       "      <th>Odometer (KM)</th>\n",
       "      <th>Doors</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Honda</td>\n",
       "      <td>White</td>\n",
       "      <td>35431</td>\n",
       "      <td>4</td>\n",
       "      <td>15323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>BMW</td>\n",
       "      <td>Blue</td>\n",
       "      <td>192714</td>\n",
       "      <td>5</td>\n",
       "      <td>19943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Honda</td>\n",
       "      <td>White</td>\n",
       "      <td>84714</td>\n",
       "      <td>4</td>\n",
       "      <td>28343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>White</td>\n",
       "      <td>154365</td>\n",
       "      <td>4</td>\n",
       "      <td>13434</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Nissan</td>\n",
       "      <td>Blue</td>\n",
       "      <td>181577</td>\n",
       "      <td>3</td>\n",
       "      <td>14043</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Make Colour  Odometer (KM)  Doors  Price\n",
       "0   Honda  White          35431      4  15323\n",
       "1     BMW   Blue         192714      5  19943\n",
       "2   Honda  White          84714      4  28343\n",
       "3  Toyota  White         154365      4  13434\n",
       "4  Nissan   Blue         181577      3  14043"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_sales.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "c2dbae90",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1000"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(car_sales)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "528fef14",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Make             object\n",
       "Colour           object\n",
       "Odometer (KM)     int64\n",
       "Doors             int64\n",
       "Price             int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_sales.dtypes\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "741ac2b4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# SPlit into x/y\n",
    "X=car_sales.drop(\"Price\",axis=1)\n",
    "Y=car_sales[\"Price\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "98dc63d4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Make</th>\n",
       "      <th>Colour</th>\n",
       "      <th>Odometer (KM)</th>\n",
       "      <th>Doors</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Honda</td>\n",
       "      <td>White</td>\n",
       "      <td>35431</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>BMW</td>\n",
       "      <td>Blue</td>\n",
       "      <td>192714</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Honda</td>\n",
       "      <td>White</td>\n",
       "      <td>84714</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>White</td>\n",
       "      <td>154365</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Nissan</td>\n",
       "      <td>Blue</td>\n",
       "      <td>181577</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Make Colour  Odometer (KM)  Doors\n",
       "0   Honda  White          35431      4\n",
       "1     BMW   Blue         192714      5\n",
       "2   Honda  White          84714      4\n",
       "3  Toyota  White         154365      4\n",
       "4  Nissan   Blue         181577      3"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "bf1c41a0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Toyota    398\n",
       "Honda     304\n",
       "Nissan    198\n",
       "BMW       100\n",
       "Name: Make, dtype: int64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_sales[\"Make\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "995ed622",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "White    407\n",
       "Blue     321\n",
       "Black     99\n",
       "Red       94\n",
       "Green     79\n",
       "Name: Colour, dtype: int64"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_sales[\"Colour\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "b7ea6b67",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4    856\n",
       "5     79\n",
       "3     65\n",
       "Name: Doors, dtype: int64"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_sales[\"Doors\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "8efae689",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    15323\n",
       "1    19943\n",
       "2    28343\n",
       "3    13434\n",
       "4    14043\n",
       "Name: Price, dtype: int64"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "6189ff41",
   "metadata": {},
   "outputs": [],
   "source": [
    "# SPlit into training and test\n",
    "X_train, X_test, Y_train, Y_test=train_test_split(X,\n",
    "                                                  Y,\n",
    "                                                  test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "a8d2b3cb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# # Build the machine learning model\n",
    "# from sklearn.ensemble import RandomForestRegressor\n",
    "\n",
    "# model=RandomForestRegressor()\n",
    "# model.fit(X_train, Y_train)\n",
    "# model.score(X_test, Y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "c52aabf5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.00000e+00, 1.00000e+00, 0.00000e+00, ..., 1.00000e+00,\n",
       "        0.00000e+00, 3.54310e+04],\n",
       "       [1.00000e+00, 0.00000e+00, 0.00000e+00, ..., 0.00000e+00,\n",
       "        1.00000e+00, 1.92714e+05],\n",
       "       [0.00000e+00, 1.00000e+00, 0.00000e+00, ..., 1.00000e+00,\n",
       "        0.00000e+00, 8.47140e+04],\n",
       "       ...,\n",
       "       [0.00000e+00, 0.00000e+00, 1.00000e+00, ..., 1.00000e+00,\n",
       "        0.00000e+00, 6.66040e+04],\n",
       "       [0.00000e+00, 1.00000e+00, 0.00000e+00, ..., 1.00000e+00,\n",
       "        0.00000e+00, 2.15883e+05],\n",
       "       [0.00000e+00, 0.00000e+00, 0.00000e+00, ..., 1.00000e+00,\n",
       "        0.00000e+00, 2.48360e+05]])"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Convert string to numerical data\n",
    "\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "from sklearn.compose import ColumnTransformer\n",
    "\n",
    "categorical_features=[\"Make\",\"Colour\",\"Doors\"]\n",
    "one_hot=OneHotEncoder()\n",
    "transformer=ColumnTransformer([(\"one_hot\",\n",
    "                                 one_hot,\n",
    "                               categorical_features)],\n",
    "                               remainder=\"passthrough\")\n",
    "transformed_X=transformer.fit_transform(X)\n",
    "transformed_X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "9e851a6d",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "      <th>12</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>35431.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>192714.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>84714.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>154365.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>181577.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>995</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>35820.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>155144.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>66604.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>215883.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>248360.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      0    1    2    3    4    5    6    7    8    9    10   11        12\n",
       "0    0.0  1.0  0.0  0.0  0.0  0.0  0.0  0.0  1.0  0.0  1.0  0.0   35431.0\n",
       "1    1.0  0.0  0.0  0.0  0.0  1.0  0.0  0.0  0.0  0.0  0.0  1.0  192714.0\n",
       "2    0.0  1.0  0.0  0.0  0.0  0.0  0.0  0.0  1.0  0.0  1.0  0.0   84714.0\n",
       "3    0.0  0.0  0.0  1.0  0.0  0.0  0.0  0.0  1.0  0.0  1.0  0.0  154365.0\n",
       "4    0.0  0.0  1.0  0.0  0.0  1.0  0.0  0.0  0.0  1.0  0.0  0.0  181577.0\n",
       "..   ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...       ...\n",
       "995  0.0  0.0  0.0  1.0  1.0  0.0  0.0  0.0  0.0  0.0  1.0  0.0   35820.0\n",
       "996  0.0  0.0  1.0  0.0  0.0  0.0  0.0  0.0  1.0  1.0  0.0  0.0  155144.0\n",
       "997  0.0  0.0  1.0  0.0  0.0  1.0  0.0  0.0  0.0  0.0  1.0  0.0   66604.0\n",
       "998  0.0  1.0  0.0  0.0  0.0  0.0  0.0  0.0  1.0  0.0  1.0  0.0  215883.0\n",
       "999  0.0  0.0  0.0  1.0  0.0  1.0  0.0  0.0  0.0  0.0  1.0  0.0  248360.0\n",
       "\n",
       "[1000 rows x 13 columns]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(transformed_X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "92d663fd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "      <th></th>\n",
       "      <th>Doors</th>\n",
       "      <th>Make_BMW</th>\n",
       "      <th>Make_Honda</th>\n",
       "      <th>Make_Nissan</th>\n",
       "      <th>Make_Toyota</th>\n",
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       "      <th>997</th>\n",
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       "</table>\n",
       "<p>1000 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Doors  Make_BMW  Make_Honda  Make_Nissan  Make_Toyota  Colour_Black  \\\n",
       "0        4         0           1            0            0             0   \n",
       "1        5         1           0            0            0             0   \n",
       "2        4         0           1            0            0             0   \n",
       "3        4         0           0            0            1             0   \n",
       "4        3         0           0            1            0             0   \n",
       "..     ...       ...         ...          ...          ...           ...   \n",
       "995      4         0           0            0            1             1   \n",
       "996      3         0           0            1            0             0   \n",
       "997      4         0           0            1            0             0   \n",
       "998      4         0           1            0            0             0   \n",
       "999      4         0           0            0            1             0   \n",
       "\n",
       "     Colour_Blue  Colour_Green  Colour_Red  Colour_White  \n",
       "0              0             0           0             1  \n",
       "1              1             0           0             0  \n",
       "2              0             0           0             1  \n",
       "3              0             0           0             1  \n",
       "4              1             0           0             0  \n",
       "..           ...           ...         ...           ...  \n",
       "995            0             0           0             0  \n",
       "996            0             0           0             1  \n",
       "997            1             0           0             0  \n",
       "998            0             0           0             1  \n",
       "999            1             0           0             0  \n",
       "\n",
       "[1000 rows x 10 columns]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dummies=pd.get_dummies(car_sales[[\"Make\",\"Colour\",\"Doors\"]])\n",
    "dummies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "3b92b89a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# # Let's refit the model\n",
    "# np.random.seed(42)\n",
    "# # SPlit into training and test\n",
    "# X_train, X_test, Y_train, Y_test=train_test_split(transformed_X,\n",
    "#                                                   Y,\n",
    "#                                                   test_size=0.2)\n",
    "# model.fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "c0e9728e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# model.score(X_test, Y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "905564c9",
   "metadata": {},
   "source": [
    "  ### What if there were missing values?\n",
    "  \n",
    "  1.Fill them with some value(also know as imputation).\n",
    "  2.Remove the sample with missing data altogether.\n",
    "  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "95b5a582",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>Make</th>\n",
       "      <th>Colour</th>\n",
       "      <th>Odometer (KM)</th>\n",
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       "  <tbody>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>BMW</td>\n",
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       "      <td>19943.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
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       "      <td>13434.0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Nissan</td>\n",
       "      <td>Blue</td>\n",
       "      <td>181577.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>14043.0</td>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Make Colour  Odometer (KM)  Doors    Price\n",
       "0   Honda  White        35431.0    4.0  15323.0\n",
       "1     BMW   Blue       192714.0    5.0  19943.0\n",
       "2   Honda  White        84714.0    4.0  28343.0\n",
       "3  Toyota  White       154365.0    4.0  13434.0\n",
       "4  Nissan   Blue       181577.0    3.0  14043.0"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Import car sales missing data\n",
    "car_sales_missing=pd.read_csv(\"csv/car-sales-extended-missing-data.csv\")\n",
    "car_sales_missing.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "01d0c860",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Make             49\n",
       "Colour           50\n",
       "Odometer (KM)    50\n",
       "Doors            50\n",
       "Price            50\n",
       "dtype: int64"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_sales_missing.isna().sum()  # Tell how many na values are there"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "fcaf0693",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create X1 & Y1\n",
    "X=car_sales_missing.drop(\"Price\",axis=1)\n",
    "Y=car_sales_missing[\"Price\"]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "b9a589dd",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Let's try and convert our data to numbers\n",
    "\n",
    "# from sklearn.preprocessing import OneHotEncoder\n",
    "# from sklearn.compose import ColumnTransformer\n",
    "\n",
    "# categorical_features=[\"Make\",\"Colour\",\"Doors\"]\n",
    "# one_hot=OneHotEncoder()\n",
    "# transformer=ColumnTransformer([(\"one_hot\",\n",
    "#                                  one_hot,\n",
    "#                                categorical_features)],\n",
    "#                                remainder=\"passthrough\")\n",
    "# transformed_X=transformer.fit_transform(X)\n",
    "# transformed_X"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9e76fef6",
   "metadata": {},
   "source": [
    "##  Fill Missing data with pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "0e65b08a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fill the \"Make\" column\n",
    "car_sales_missing[\"Make\"].fillna(\"missing\",inplace=True)\n",
    "\n",
    "## Fill the \"Colour\" column\n",
    "car_sales_missing[\"Colour\"].fillna(\"missing\", inplace=True)\n",
    "\n",
    "## Fill the \"Odometer\" column\n",
    "car_sales_missing[\"Odometer (KM)\"].fillna(car_sales_missing[\"Odometer (KM)\"].mean(), inplace=True)\n",
    "\n",
    "# Fill the \"Doors\" column.\n",
    "car_sales_missing[\"Doors\"].fillna(car_sales_missing[\"Doors\"].mean(),inplace=True)\n",
    "\n",
    "# # Fill the \"Price\" column\n",
    "# car_sales_missing[\"Price\"].filna(car_sales_missing[\"Price\"].mean(),inplace=True)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "885c402e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Make              0\n",
       "Colour            0\n",
       "Odometer (KM)     0\n",
       "Doors             0\n",
       "Price            50\n",
       "dtype: int64"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the dataframe again\n",
    "car_sales_missing.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "45c4c557",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Remove rows with missing Price value\n",
    "car_sales_missing.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "79482782",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Make             0\n",
       "Colour           0\n",
       "Odometer (KM)    0\n",
       "Doors            0\n",
       "Price            0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_sales_missing.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "8f60e9c1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "950"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(car_sales_missing)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "bacd7e3c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<950x17 sparse matrix of type '<class 'numpy.float64'>'\n",
       "\twith 4750 stored elements in Compressed Sparse Row format>"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "from sklearn.compose import ColumnTransformer\n",
    "\n",
    "categorical_features=[\"Make\",\"Colour\",\"Doors\"]\n",
    "one_hot=OneHotEncoder()\n",
    "transformer=ColumnTransformer([(\"one_hot\",\n",
    "                                 one_hot,\n",
    "                               categorical_features)],\n",
    "                               remainder=\"passthrough\")\n",
    "transformed_X=transformer.fit_transform(car_sales_missing)\n",
    "transformed_X"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "431a86a3",
   "metadata": {},
   "source": [
    "## 3 Option 2 : fill value with scikit-learn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "e76f92cf",
   "metadata": {},
   "outputs": [],
   "source": [
    "car_sales_missing=pd.read_csv(\"csv/car-sales-extended-missing-data.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "f89dc51b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Make</th>\n",
       "      <th>Colour</th>\n",
       "      <th>Odometer (KM)</th>\n",
       "      <th>Doors</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Honda</td>\n",
       "      <td>White</td>\n",
       "      <td>35431.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>15323.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>BMW</td>\n",
       "      <td>Blue</td>\n",
       "      <td>192714.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>19943.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Honda</td>\n",
       "      <td>White</td>\n",
       "      <td>84714.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>28343.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>White</td>\n",
       "      <td>154365.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>13434.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Nissan</td>\n",
       "      <td>Blue</td>\n",
       "      <td>181577.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>14043.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Make Colour  Odometer (KM)  Doors    Price\n",
       "0   Honda  White        35431.0    4.0  15323.0\n",
       "1     BMW   Blue       192714.0    5.0  19943.0\n",
       "2   Honda  White        84714.0    4.0  28343.0\n",
       "3  Toyota  White       154365.0    4.0  13434.0\n",
       "4  Nissan   Blue       181577.0    3.0  14043.0"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_sales_missing.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "8560f28e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Make             49\n",
       "Colour           50\n",
       "Odometer (KM)    50\n",
       "Doors            50\n",
       "Price            50\n",
       "dtype: int64"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_sales_missing.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "a8a71edf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Make             47\n",
       "Colour           46\n",
       "Odometer (KM)    48\n",
       "Doors            47\n",
       "Price             0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Drop the rows with no labels\n",
    "car_sales_missing.dropna(subset=[\"Price\"], inplace=True)\n",
    "car_sales_missing.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "d30d722a",
   "metadata": {},
   "outputs": [],
   "source": [
    "#SPlit into X & Y\n",
    "X=car_sales_missing.drop(\"Price\", axis=1)\n",
    "Y=car_sales_missing[\"Price\"]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "d8df7e4d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([['Honda', 'White', 4.0, 35431.0],\n",
       "       ['BMW', 'Blue', 5.0, 192714.0],\n",
       "       ['Honda', 'White', 4.0, 84714.0],\n",
       "       ...,\n",
       "       ['Nissan', 'Blue', 4.0, 66604.0],\n",
       "       ['Honda', 'White', 4.0, 215883.0],\n",
       "       ['Toyota', 'Blue', 4.0, 248360.0]], dtype=object)"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Filling mssing values with Scikit learn\n",
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.compose import ColumnTransformer\n",
    "\n",
    "#Fill categorical values with \"missing\" & numerical values with mean\n",
    "cat_imputer= SimpleImputer(strategy=\"constant\", fill_value=\"missing\")\n",
    "door_imputer=SimpleImputer(strategy=\"constant\", fill_value=4)\n",
    "num_imputer=SimpleImputer(strategy=\"mean\")\n",
    "\n",
    "#Define columns\n",
    "cat_features= [\"Make\",\"Colour\"]\n",
    "door_feature=[\"Doors\"]\n",
    "num_features=[\"Odometer (KM)\"]\n",
    "\n",
    "# Create an imputer (something that fills missing data)\n",
    "imputer=ColumnTransformer([\n",
    "    (\"cat_imputer\", cat_imputer, cat_features),\n",
    "    (\"door_imputer\", door_imputer, door_feature),\n",
    "    (\"num_imputer\", num_imputer, num_features)\n",
    "])\n",
    "\n",
    "\n",
    "#Transform the data\n",
    "\n",
    "filled_X= imputer.fit_transform(X)\n",
    "filled_X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "9fe680d4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Make</th>\n",
       "      <th>Colour</th>\n",
       "      <th>Doors</th>\n",
       "      <th>Odometer (KM)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Honda</td>\n",
       "      <td>White</td>\n",
       "      <td>4.0</td>\n",
       "      <td>35431.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>BMW</td>\n",
       "      <td>Blue</td>\n",
       "      <td>5.0</td>\n",
       "      <td>192714.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Honda</td>\n",
       "      <td>White</td>\n",
       "      <td>4.0</td>\n",
       "      <td>84714.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>White</td>\n",
       "      <td>4.0</td>\n",
       "      <td>154365.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Nissan</td>\n",
       "      <td>Blue</td>\n",
       "      <td>3.0</td>\n",
       "      <td>181577.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Make Colour Doors Odometer (KM)\n",
       "0   Honda  White   4.0       35431.0\n",
       "1     BMW   Blue   5.0      192714.0\n",
       "2   Honda  White   4.0       84714.0\n",
       "3  Toyota  White   4.0      154365.0\n",
       "4  Nissan   Blue   3.0      181577.0"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_sales_filled=pd.DataFrame(filled_X,\n",
    "                              columns=[\"Make\", \"Colour\",\"Doors\",\"Odometer (KM)\"\n",
    "                                     ])\n",
    "car_sales_filled.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "75958d8b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Make             0\n",
       "Colour           0\n",
       "Doors            0\n",
       "Odometer (KM)    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_sales_filled.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "f151c780",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Make</th>\n",
       "      <th>Colour</th>\n",
       "      <th>Doors</th>\n",
       "      <th>Odometer (KM)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Honda</td>\n",
       "      <td>White</td>\n",
       "      <td>4.0</td>\n",
       "      <td>35431.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>BMW</td>\n",
       "      <td>Blue</td>\n",
       "      <td>5.0</td>\n",
       "      <td>192714.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Honda</td>\n",
       "      <td>White</td>\n",
       "      <td>4.0</td>\n",
       "      <td>84714.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>White</td>\n",
       "      <td>4.0</td>\n",
       "      <td>154365.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Nissan</td>\n",
       "      <td>Blue</td>\n",
       "      <td>3.0</td>\n",
       "      <td>181577.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Make Colour Doors Odometer (KM)\n",
       "0   Honda  White   4.0       35431.0\n",
       "1     BMW   Blue   5.0      192714.0\n",
       "2   Honda  White   4.0       84714.0\n",
       "3  Toyota  White   4.0      154365.0\n",
       "4  Nissan   Blue   3.0      181577.0"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_sales_filled.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "4c35597d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<950x15 sparse matrix of type '<class 'numpy.float64'>'\n",
       "\twith 3800 stored elements in Compressed Sparse Row format>"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import OneHotEncoder\n",
    "from sklearn.compose import ColumnTransformer\n",
    "\n",
    "categorical_features=[\"Make\",\"Colour\",\"Doors\"]\n",
    "one_hot=OneHotEncoder()\n",
    "transformer=ColumnTransformer([(\"one_hot\",\n",
    "                                 one_hot,\n",
    "                                 categorical_features)],\n",
    "                                 remainder=\"passthrough\")\n",
    "transformed_X=transformer.fit_transform(car_sales_filled)\n",
    "transformed_X\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "344998a4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.21990196728583944"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Now ew;ve got our data as number and filled (no missing values)\n",
    "# Let's fit a model\n",
    "\n",
    "np.random.seed(42)\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, Y_train, Y_test=train_test_split(transformed_X,\n",
    "                                                      Y,\n",
    "                                                      test_size=0.2)\n",
    "model=RandomForestRegressor(n_estimators=100)\n",
    "model.fit(X_train, Y_train)\n",
    "model.score(X_test, Y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "66fbae5d",
   "metadata": {},
   "source": [
    "## 2. Choosing the right estimstor/algorithm for our problem\n",
    "\n",
    "Scikit_Learn uses estimator as another term for machine learning model or algorithm\n",
    "\n",
    "* Classification:- Predicting whether a sample is one thing or another\n",
    "* Regression:- Predicting a number"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a6a7d6c9",
   "metadata": {},
   "source": [
    "## 2.1 Picking a machine learning model for a regression problem"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "ce4c3274",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "heart_disease=pd.read_csv(\"csv/11.2 heart-disease.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "438ac98f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>cp</th>\n",
       "      <th>trestbps</th>\n",
       "      <th>chol</th>\n",
       "      <th>fbs</th>\n",
       "      <th>restecg</th>\n",
       "      <th>thalach</th>\n",
       "      <th>exang</th>\n",
       "      <th>oldpeak</th>\n",
       "      <th>slope</th>\n",
       "      <th>ca</th>\n",
       "      <th>thal</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>63</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>145</td>\n",
       "      <td>233</td>\n",
       "      <td>1</td>\n",
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       "      <td>150</td>\n",
       "      <td>0</td>\n",
       "      <td>2.3</td>\n",
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       "      <th>1</th>\n",
       "      <td>37</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>130</td>\n",
       "      <td>250</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>187</td>\n",
       "      <td>0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>41</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>130</td>\n",
       "      <td>204</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>172</td>\n",
       "      <td>0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>56</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>120</td>\n",
       "      <td>236</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>178</td>\n",
       "      <td>0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>120</td>\n",
       "      <td>354</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>163</td>\n",
       "      <td>1</td>\n",
       "      <td>0.6</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  slope  \\\n",
       "0   63    1   3       145   233    1        0      150      0      2.3      0   \n",
       "1   37    1   2       130   250    0        1      187      0      3.5      0   \n",
       "2   41    0   1       130   204    0        0      172      0      1.4      2   \n",
       "3   56    1   1       120   236    0        1      178      0      0.8      2   \n",
       "4   57    0   0       120   354    0        1      163      1      0.6      2   \n",
       "\n",
       "   ca  thal  target  \n",
       "0   0     1       1  \n",
       "1   0     2       1  \n",
       "2   0     2       1  \n",
       "3   0     2       1  \n",
       "4   0     2       1  "
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "heart_disease.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "9db356b1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "303"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "  len(heart_disease)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "834c1a8a",
   "metadata": {},
   "source": [
    " ##Consulting the map and it says to try `LinearSVC` .\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "64348246",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8688524590163934"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Import the LinearSVC estimator class \n",
    "from sklearn.svm import LinearSVC\n",
    "\n",
    "# Setup random seed \n",
    "np.random.seed(42)\n",
    "\n",
    "#Make the data \n",
    "\n",
    "X=heart_disease.drop(\"target\", axis=1)\n",
    "Y=heart_disease[\"target\"]\n",
    "\n",
    "#Split the data\n",
    "\n",
    "X_train, X_test, Y_train, Y_test=train_test_split(X,Y, test_size=0.2)\n",
    "\n",
    "#Instantiate LinearSVC\n",
    "clf=LinearSVC()\n",
    "clf.fit(X_train, Y_train)\n",
    "\n",
    "# Evaluate the LinearSVC\n",
    "\n",
    "clf.score(X_test, Y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "08cb8dd3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    165\n",
       "0    138\n",
       "Name: target, dtype: int64"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "heart_disease[\"target\"].value_counts() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "1815fbd2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8524590163934426"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Import the RandomForestClassifier estimator class \n",
    "from sklearn.svm import LinearSVC\n",
    "\n",
    "# Setup random seed \n",
    "np.random.seed(42)\n",
    "\n",
    "#Make the data \n",
    "\n",
    "X=heart_disease.drop(\"target\", axis=1)\n",
    "Y=heart_disease[\"target\"]\n",
    "\n",
    "#Split the data\n",
    "\n",
    "X_train, X_test, Y_train, Y_test=train_test_split(X,Y, test_size=0.2)\n",
    "\n",
    "#Instantiate Random Forest Classifier\n",
    "clf=RandomForestClassifier(n_estimators=100)\n",
    "clf.fit(X_train, Y_train)\n",
    "\n",
    "# Evaluate the Random Forest Classifier\n",
    "clf.score(X_test, Y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e2e26216",
   "metadata": {},
   "source": [
    "Tidbit:\n",
    "      \n",
    "        \n",
    "        1. If you have structured data, used ensemble method \n",
    "        2. If you have unstructured data, use deeop learning or transfer learning "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "b5ad145f",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>354</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>163</td>\n",
       "      <td>1</td>\n",
       "      <td>0.6</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>57</td>\n",
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       "      <td>0</td>\n",
       "      <td>140</td>\n",
       "      <td>241</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>123</td>\n",
       "      <td>1</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>299</th>\n",
       "      <td>45</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>110</td>\n",
       "      <td>264</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>132</td>\n",
       "      <td>0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>300</th>\n",
       "      <td>68</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>144</td>\n",
       "      <td>193</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>141</td>\n",
       "      <td>0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>301</th>\n",
       "      <td>57</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>130</td>\n",
       "      <td>131</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>115</td>\n",
       "      <td>1</td>\n",
       "      <td>1.2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>302</th>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>130</td>\n",
       "      <td>236</td>\n",
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       "      <td>0</td>\n",
       "      <td>174</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>303 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  \\\n",
       "0     63    1   3       145   233    1        0      150      0      2.3   \n",
       "1     37    1   2       130   250    0        1      187      0      3.5   \n",
       "2     41    0   1       130   204    0        0      172      0      1.4   \n",
       "3     56    1   1       120   236    0        1      178      0      0.8   \n",
       "4     57    0   0       120   354    0        1      163      1      0.6   \n",
       "..   ...  ...  ..       ...   ...  ...      ...      ...    ...      ...   \n",
       "298   57    0   0       140   241    0        1      123      1      0.2   \n",
       "299   45    1   3       110   264    0        1      132      0      1.2   \n",
       "300   68    1   0       144   193    1        1      141      0      3.4   \n",
       "301   57    1   0       130   131    0        1      115      1      1.2   \n",
       "302   57    0   1       130   236    0        0      174      0      0.0   \n",
       "\n",
       "     slope  ca  thal  target  \n",
       "0        0   0     1       1  \n",
       "1        0   0     2       1  \n",
       "2        2   0     2       1  \n",
       "3        2   0     2       1  \n",
       "4        2   0     2       1  \n",
       "..     ...  ..   ...     ...  \n",
       "298      1   0     3       0  \n",
       "299      1   0     3       0  \n",
       "300      1   2     3       0  \n",
       "301      1   1     3       0  \n",
       "302      1   1     2       0  \n",
       "\n",
       "[303 rows x 14 columns]"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "heart_disease"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d15cb518",
   "metadata": {},
   "source": [
    "what _were_covering\n",
    "\n",
    "0.An end-to-end Scikit-learn workflow  \n",
    "1.Getting the data ready \n",
    "2.Choose the right estimator/algorithm and use it to make predictions on our data \n",
    "3.Fit the model /algorithm amd use it to make prediction on our data \n",
    "4.Evaluating a model \n",
    "5.Improve a model \n",
    "6.Save and load a tained model \n",
    "7.Putting it all togrther!\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "026c5d10",
   "metadata": {},
   "source": [
    "## 3. Fit the model/algorithm and use it to make prediction on our data \n",
    "\n",
    "### 3.1 fitting the model to the data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "a6c2b83d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8524590163934426"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Import the RandomClassifier estimator class \n",
    "\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "\n",
    "#Setup random seed\n",
    "np.random.seed(42)\n",
    "\n",
    "# MAke the data\n",
    "x= heart_disease.drop(\"target\",  axis=1)\n",
    "y=heart_disease[\"target\"]\n",
    "\n",
    "#Split the data\n",
    "x_train, x_test, y_train, y_test= train_test_split(x, y, test_size=0.2)\n",
    "\n",
    "#initaiate Random Forest Classifier \n",
    "clf=RandomForestClassifier(n_estimators=100)\n",
    "\n",
    "#Fit the model to the data(Trianing thr machine learning model)\n",
    "clf.fit(x_train, y_train)\n",
    "\n",
    "#Evaluate the Random Forest Classifier(use the patterns the model has learned)\n",
    "clf.score(x_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "b64c1723",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>cp</th>\n",
       "      <th>trestbps</th>\n",
       "      <th>chol</th>\n",
       "      <th>fbs</th>\n",
       "      <th>restecg</th>\n",
       "      <th>thalach</th>\n",
       "      <th>exang</th>\n",
       "      <th>oldpeak</th>\n",
       "      <th>slope</th>\n",
       "      <th>ca</th>\n",
       "      <th>thal</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>63</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>145</td>\n",
       "      <td>233</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>150</td>\n",
       "      <td>0</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>37</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>130</td>\n",
       "      <td>250</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>187</td>\n",
       "      <td>0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>41</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>130</td>\n",
       "      <td>204</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>172</td>\n",
       "      <td>0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>56</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>120</td>\n",
       "      <td>236</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>178</td>\n",
       "      <td>0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>120</td>\n",
       "      <td>354</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>163</td>\n",
       "      <td>1</td>\n",
       "      <td>0.6</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  slope  \\\n",
       "0   63    1   3       145   233    1        0      150      0      2.3      0   \n",
       "1   37    1   2       130   250    0        1      187      0      3.5      0   \n",
       "2   41    0   1       130   204    0        0      172      0      1.4      2   \n",
       "3   56    1   1       120   236    0        1      178      0      0.8      2   \n",
       "4   57    0   0       120   354    0        1      163      1      0.6      2   \n",
       "\n",
       "   ca  thal  \n",
       "0   0     1  \n",
       "1   0     2  \n",
       "2   0     2  \n",
       "3   0     2  \n",
       "4   0     2  "
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "e4f33642",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1\n",
       "1    1\n",
       "2    1\n",
       "3    1\n",
       "4    1\n",
       "Name: target, dtype: int64"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a5f6fa49",
   "metadata": {},
   "source": [
    "### 3.2 Make prediction using a machine learning model\n",
    "\n",
    "2 ways to make prediction\n",
    "\n",
    "1. predict()\n",
    "2. predict_proba()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "a7fecc3f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>cp</th>\n",
       "      <th>trestbps</th>\n",
       "      <th>chol</th>\n",
       "      <th>fbs</th>\n",
       "      <th>restecg</th>\n",
       "      <th>thalach</th>\n",
       "      <th>exang</th>\n",
       "      <th>oldpeak</th>\n",
       "      <th>slope</th>\n",
       "      <th>ca</th>\n",
       "      <th>thal</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>179</th>\n",
       "      <td>57</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>150</td>\n",
       "      <td>276</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>112</td>\n",
       "      <td>1</td>\n",
       "      <td>0.6</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>228</th>\n",
       "      <td>59</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>170</td>\n",
       "      <td>288</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>159</td>\n",
       "      <td>0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111</th>\n",
       "      <td>57</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>150</td>\n",
       "      <td>126</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>173</td>\n",
       "      <td>0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>246</th>\n",
       "      <td>56</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>134</td>\n",
       "      <td>409</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>150</td>\n",
       "      <td>1</td>\n",
       "      <td>1.9</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>71</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>110</td>\n",
       "      <td>265</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>130</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>249</th>\n",
       "      <td>69</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>140</td>\n",
       "      <td>254</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>146</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>129</td>\n",
       "      <td>196</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>163</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>300</th>\n",
       "      <td>68</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>144</td>\n",
       "      <td>193</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>141</td>\n",
       "      <td>0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193</th>\n",
       "      <td>60</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>145</td>\n",
       "      <td>282</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>142</td>\n",
       "      <td>1</td>\n",
       "      <td>2.8</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>184</th>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>150</td>\n",
       "      <td>243</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>128</td>\n",
       "      <td>0</td>\n",
       "      <td>2.6</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>61 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  \\\n",
       "179   57    1   0       150   276    0        0      112      1      0.6   \n",
       "228   59    1   3       170   288    0        0      159      0      0.2   \n",
       "111   57    1   2       150   126    1        1      173      0      0.2   \n",
       "246   56    0   0       134   409    0        0      150      1      1.9   \n",
       "60    71    0   2       110   265    1        0      130      0      0.0   \n",
       "..   ...  ...  ..       ...   ...  ...      ...      ...    ...      ...   \n",
       "249   69    1   2       140   254    0        0      146      0      2.0   \n",
       "104   50    1   2       129   196    0        1      163      0      0.0   \n",
       "300   68    1   0       144   193    1        1      141      0      3.4   \n",
       "193   60    1   0       145   282    0        0      142      1      2.8   \n",
       "184   50    1   0       150   243    0        0      128      0      2.6   \n",
       "\n",
       "     slope  ca  thal  \n",
       "179      1   1     1  \n",
       "228      1   0     3  \n",
       "111      2   1     3  \n",
       "246      1   2     3  \n",
       "60       2   1     2  \n",
       "..     ...  ..   ...  \n",
       "249      1   3     3  \n",
       "104      2   0     2  \n",
       "300      1   2     3  \n",
       "193      1   2     3  \n",
       "184      1   0     3  \n",
       "\n",
       "[61 rows x 13 columns]"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_test\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "4b61227c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 0, 1, 0, 1, 1, 1, 0, 0, 0, 0, 0,\n",
       "       1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0], dtype=int64)"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "45adfa70",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 0, 1, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0, 1, 0,\n",
       "        0, 1, 1, 0, 0, 1, 0, 1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "        1, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0]], dtype=int64)"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array([y_test])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "a3992b31",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8524590163934426"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Compare prediction to truth labels to evaluate the model\n",
    "y_preds=clf.predict(x_test)\n",
    "np.mean(y_preds==y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "c82be925",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8524590163934426"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.score(x_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "a6a21f28",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8524590163934426"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "accuracy_score(y_test, y_preds)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "917eef04",
   "metadata": {},
   "source": [
    "MAke the predict_proba"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "485f61d6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.89, 0.11],\n",
       "       [0.49, 0.51],\n",
       "       [0.43, 0.57],\n",
       "       [0.84, 0.16],\n",
       "       [0.18, 0.82]])"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Predict_proba() returns probabilities of a classification label\n",
    "\n",
    "clf.predict_proba(x_test[:5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "9bac564c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 1, 0, 1], dtype=int64)"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's predict() on the same data..\n",
    "\n",
    "clf.predict(x_test[:5]) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "35e5e18c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    165\n",
       "0    138\n",
       "Name: target, dtype: int64"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "heart_disease[\"target\"].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8028ca3b",
   "metadata": {},
   "source": [
    "## 4 .Evaluating a machine learning model\n",
    "\n",
    "\n",
    "Three ways to evaluate Scikit_learn models/estimators:\n",
    "\n",
    "1.Estimators `score` method` \n",
    "2.The `scoring parameter\n",
    "3.Problem-specific metric functions.\n",
    "\n",
    "\n",
    "## 4.1 Evaluate the model with score method\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "4d688f7e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestClassifier()"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "np.random.seed(42)\n",
    "\n",
    "x=heart_disease.drop(\"target\", axis=1)\n",
    "y=heart_disease[\"target\"]\n",
    "\n",
    "x_train, x_test, y_train, y_test=train_test_split(x, y, test_size=0.2)\n",
    "\n",
    "clf=RandomForestClassifier()\n",
    "\n",
    "clf.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "24674e62",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.score(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "d3db6861",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8524590163934426"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.score(x_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8ea95504",
   "metadata": {},
   "source": [
    "## 4.2   Evaluating a model using the scoring parameter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "4f84064b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestClassifier()"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "np.random.seed(42)\n",
    "\n",
    "x=heart_disease.drop(\"target\", axis=1)\n",
    "y=heart_disease[\"target\"]\n",
    "\n",
    "x_train, x_test, y_train, y_test=train_test_split(x, y, test_size=0.2)\n",
    "\n",
    "clf=RandomForestClassifier(n_estimators=100)\n",
    "\n",
    "clf.fit(x_train, y_train)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "ed088f3f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8524590163934426"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.score(x_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "09eea26a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.90322581, 0.80645161, 0.87096774, 0.9       , 0.86666667,\n",
       "       0.76666667, 0.7       , 0.83333333, 0.73333333, 0.8       ])"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cross_val_score(clf, x, y, cv=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "6571377a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.8524590163934426, 0.8248087431693989)"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.seed(42)\n",
    "\n",
    "# Single trainning and test split score\n",
    "clf_single_score=clf.score(x_test, y_test)\n",
    "\n",
    "# Take the mean of 5-fold cross-validation score\n",
    "clf_cross_val_score=np.mean(cross_val_score(clf, x, y, cv=5))\n",
    "\n",
    "#Compare the two\n",
    "clf_single_score, clf_cross_val_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "33e7c31d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.78688525, 0.86885246, 0.80327869, 0.78333333, 0.76666667])"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Scoring parameter set t none by default\n",
    "\n",
    "cross_val_score(clf, x, y, cv=5, scoring=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "db78a230",
   "metadata": {},
   "source": [
    "## 4.2.1  Classification mdoel evaluation mterics\n",
    "\n",
    "1. Accuracy\n",
    "2.Area under ROC curve\n",
    "3.Confusion matrix\n",
    "4.Classification report\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "a394876e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "np.random.seed(42)\n",
    "\n",
    "x=heart_disease.drop(\"target\", axis=1)\n",
    "y=heart_disease[\"target\"]\n",
    "\n",
    "clf=RandomForestClassifier(n_estimators=100)\n",
    "cross_val_score=cross_val_score(clf, x, y, cv=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "3744d402",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8248087431693989"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(cross_val_score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "6d308d75",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Heart Disease Classifier Cross-Validated Accuracy: 82.48087431693989\n"
     ]
    }
   ],
   "source": [
    "print(f\"Heart Disease Classifier Cross-Validated Accuracy: {np.mean(cross_val_score) * 100}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9a2f5beb",
   "metadata": {},
   "source": [
    "** Area under the receiver the operating characteristic curve (AUC?ROC) **\n",
    "\n",
    "* Area under curve(AUC)\n",
    "* ROC curve\n",
    "\n",
    "ROC curves are a comparison of a model's true positive rate (tpr) versuse a models false positive rate(fpr).\n",
    "\n",
    "* True positive =model predicts 1 when truth 1\n",
    "* false positive = model predicts 1 when truth is 0\n",
    "* True negative = model predicts 0 when truth is 0\n",
    "* False negative = model predicts 0 when turth is 1\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "fbae1d17",
   "metadata": {},
   "outputs": [],
   "source": [
    "#  Create x_test ... etc\n",
    "x_train, x_test, y_trian, y_test=train_test_split(x,y, test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "7edf8378",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.7 , 0.3 ],\n",
       "       [0.54, 0.46],\n",
       "       [0.75, 0.25],\n",
       "       [0.09, 0.91],\n",
       "       [0.44, 0.56],\n",
       "       [0.62, 0.38],\n",
       "       [0.39, 0.61],\n",
       "       [0.54, 0.46],\n",
       "       [0.38, 0.62],\n",
       "       [0.54, 0.46]])"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import roc_curve\n",
    "\n",
    "\n",
    "# Fit the class\n",
    "clf.fit(x_train,y_train)\n",
    "\n",
    "# Make prediction with probabilitie\n",
    "y_probs=clf.predict_proba(x_test)\n",
    "\n",
    "y_probs[:10]\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "1f3fe57c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.3 , 0.46, 0.25, 0.91, 0.56, 0.38, 0.61, 0.46, 0.62, 0.46])"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_probs_positive=y_probs[:,1]\n",
    "y_probs_positive[:10]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "2597ac3e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.        , 0.        , 0.        , 0.10344828, 0.10344828,\n",
       "       0.10344828, 0.24137931, 0.27586207, 0.31034483, 0.34482759,\n",
       "       0.37931034, 0.4137931 , 0.4137931 , 0.44827586, 0.51724138,\n",
       "       0.55172414, 0.5862069 , 0.5862069 , 0.68965517, 0.68965517,\n",
       "       0.75862069, 0.82758621, 0.86206897, 0.86206897, 0.89655172,\n",
       "       0.89655172, 0.93103448, 0.93103448, 0.93103448, 0.93103448,\n",
       "       0.96551724, 0.96551724, 1.        ])"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculaet fpr, tpr and thresholds\n",
    "fpr, tpr, thresholds= roc_curve(y_test, y_probs_positive)\n",
    "\n",
    "#Check the false positive rates\n",
    "fpr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "533d904d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a function for pltting ROC curve\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "def plot_roc_curve(fpr, tpr):\n",
    "    \"\"\"\n",
    "    Plots a ROC curve given the false positive rate (fpr)\n",
    "    and true positive rate (tpr) of a model.\n",
    "    \"\"\"\n",
    "    \n",
    "    # Plot roc curve \n",
    "    plt.plot(fpr, tpr, color=\"orange\", label=\"ROC\")\n",
    "    #Plot line woith no predictive power(baseline)\n",
    "    plt.plot([0,1], [0,1], color=\"darkblue\", linestyle=\"--\", label=\"Guessing\")\n",
    "    \n",
    "    # Customize the plot\n",
    "    plt.xlabel(\"False postitive rate (fpr)\")\n",
    "    plt.ylabel(\"True positive rate (tpr)\")\n",
    "    plt.title(\"Receiver OPerating Characteristic (ROC) curve \")\n",
    "    plt.legend()\n",
    "    plt.show()\n",
    "    \n",
    "plot_roc_curve(fpr, tpr)    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "94e3dd5c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.4326508620689655"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import roc_auc_score\n",
    "\n",
    "roc_auc_score(y_test, y_probs_positive)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "88c4e54e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# PLot perfect ROC curve and AUC score\n",
    "\n",
    "fpr, tpr, thresholds=roc_curve(y_test, y_test)\n",
    "plot_roc_curve(fpr, tpr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "34836bcc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Perfect AUC score \n",
    "\n",
    "roc_auc_score(y_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa92627e",
   "metadata": {},
   "source": [
    "** Confusion Matrix**\n",
    " A confusion matrix is a quick way to compare the labels a model predicts and the actual labels it was supposed to predict.\n",
    "    \n",
    "   in essense , gig=ving you an idea of where the model is getting confused. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "f89c041b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 9, 20],\n",
       "       [14, 18]], dtype=int64)"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "y_preds=clf.predict(x_test)\n",
    "\n",
    "confusion_matrix(y_test, y_preds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "1c2b1df0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Predicted Labels</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Actusl Labels</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>14</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Predicted Labels   0   1\n",
       "Actusl Labels           \n",
       "0                  9  20\n",
       "1                 14  18"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Visualize confusion matrix with pd.crosstab()\n",
    "\n",
    "pd.crosstab(y_test,\n",
    "           y_preds,\n",
    "           rownames=[\"Actusl Labels\"],\n",
    "           colnames=[\"Predicted Labels\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "d71fb712",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: >"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Make our confusion matrix more visual Seaborn's heatmap()\n",
    "\n",
    "import seaborn as sns\n",
    "\n",
    "\n",
    "# Set the font scale\n",
    "sns.set(font_scale=1.5)\n",
    "\n",
    "#Create a confusion matrix\n",
    "conf_mat=confusion_matrix(y_test, y_preds)\n",
    "\n",
    "#Plot it using seaborn\n",
    "sns.heatmap(conf_mat)\n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "54e9ad87",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 300x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_conf_mat(conf_mat):\n",
    "    \"\"\"\n",
    "    Plots a confusion matrix using Seaborn's heatmap().\n",
    "    \"\"\"\n",
    "    \n",
    "    fig, ax=plt.subplots(figsize=(3,3))\n",
    "    ax=sns.heatmap(conf_mat,\n",
    "                   annot=True, # Annotate the boxes with conf_mat info)\n",
    "                   cbar=False)\n",
    "    plt.xlabel=(\"True label\")\n",
    "    plt.ylabel(\"Predicted label\");\n",
    "    \n",
    "plot_conf_mat(conf_mat)    "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1ef810ec",
   "metadata": {},
   "source": [
    "## Classification Report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "59534384",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.39      0.31      0.35        29\n",
      "           1       0.47      0.56      0.51        32\n",
      "\n",
      "    accuracy                           0.44        61\n",
      "   macro avg       0.43      0.44      0.43        61\n",
      "weighted avg       0.43      0.44      0.43        61\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "\n",
    "print(classification_report(y_test,y_preds ))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "7f0223a7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0.0</th>\n",
       "      <th>1.0</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>macro avg</th>\n",
       "      <th>weighted avg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>precision</th>\n",
       "      <td>0.99990</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.9999</td>\n",
       "      <td>0.499950</td>\n",
       "      <td>0.99980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>recall</th>\n",
       "      <td>1.00000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.9999</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.99990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>f1-score</th>\n",
       "      <td>0.99995</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.9999</td>\n",
       "      <td>0.499975</td>\n",
       "      <td>0.99985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>support</th>\n",
       "      <td>9999.00000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.9999</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.00000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  0.0  1.0  accuracy     macro avg  weighted avg\n",
       "precision     0.99990  0.0    0.9999      0.499950       0.99980\n",
       "recall        1.00000  0.0    0.9999      0.500000       0.99990\n",
       "f1-score      0.99995  0.0    0.9999      0.499975       0.99985\n",
       "support    9999.00000  1.0    0.9999  10000.000000   10000.00000"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Where precision and recall become valuable\n",
    "\n",
    "disease_true=np.zeros(10000)\n",
    "disease_true[0]=1 #omly one positive case\n",
    "\n",
    "disease_preds= np.zeros(10000) # Model predicts every case as 0\n",
    "\n",
    "pd.DataFrame(classification_report(disease_true,\n",
    "                                   disease_preds,\n",
    "                                  output_dict=True))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3e3549f0",
   "metadata": {},
   "source": [
    " ## 4.3 Using different metrics as Scikit-Learn function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "7b66c18a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classifier metrics in the test set\n",
      "Accuracy: 85.25%\n",
      "Precision: 0.8484848484848485\n",
      "Recall: 0.875\n",
      "F1: 0.8615384615384615\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score\n",
    "from sklearn.ensemble import RandomForestClassifier \n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "np.random.seed(42)\n",
    "\n",
    "x=heart_disease.drop(\"target\", axis=1)\n",
    "y=heart_disease[\"target\"]\n",
    "\n",
    "x_train, x_test, y_train, y_test= train_test_split(x, y, test_size=0.2)\n",
    "\n",
    "clf=RandomForestClassifier(n_estimators=100)\n",
    "clf.fit(x_train, y_train)\n",
    "\n",
    "\n",
    "# Make some prediction\n",
    "\n",
    "y_preds=clf.predict(x_test)\n",
    "\n",
    "# Evaluate the classifier\n",
    "\n",
    "print(\"Classifier metrics in the test set\")\n",
    "print(f\"Accuracy: {accuracy_score(y_test, y_preds)*100:.2f}%\")\n",
    "print(f\"Precision: {precision_score(y_test, y_preds)}\")\n",
    "print(f\"Recall: {recall_score(y_test, y_preds)}\")\n",
    "print(f\"F1: {f1_score(y_test, y_preds)}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3efca06d",
   "metadata": {},
   "source": [
    "## 5. Improving a model \n",
    "\n",
    "First prediction = baseline predictions.\n",
    "first model= baseline model.\n",
    "\n",
    "\n",
    "from a data perspective:\n",
    "* Could we collect more data?(generally, the more data , the better)\n",
    "* Could we improve our data?\n",
    "\n",
    "\n",
    "From a model perspective:\n",
    "* Is there a better model we could use?\n",
    "* Could we improve the current model?\n",
    "\n",
    "\n",
    "Hyperparameters vs Parameters  \n",
    "* Parameter = model find this pattern in data\n",
    "* Hyperparameters= setting on a model you can adjust to (potentially) improve its ability to find patterns\n",
    "\n",
    "\n",
    "Three ways to adjust the hyperparameter\n",
    "1. By hand\n",
    "2. Randomly with RandomSearchCV\n",
    "3. Exhaustively with GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "f1e7512d",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "clf=RandomForestClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "a6cf0da7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'bootstrap': True,\n",
       " 'ccp_alpha': 0.0,\n",
       " 'class_weight': None,\n",
       " 'criterion': 'gini',\n",
       " 'max_depth': None,\n",
       " 'max_features': 'sqrt',\n",
       " 'max_leaf_nodes': None,\n",
       " 'max_samples': None,\n",
       " 'min_impurity_decrease': 0.0,\n",
       " 'min_samples_leaf': 1,\n",
       " 'min_samples_split': 2,\n",
       " 'min_weight_fraction_leaf': 0.0,\n",
       " 'n_estimators': 100,\n",
       " 'n_jobs': None,\n",
       " 'oob_score': False,\n",
       " 'random_state': None,\n",
       " 'verbose': 0,\n",
       " 'warm_start': False}"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.get_params()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "664d3b36",
   "metadata": {},
   "source": [
    "###  5.1 Tunnig hyperparameters by hand\n",
    "\n",
    "Let's make 3 sets, training ,validation, test.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "9e227d46",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'bootstrap': True,\n",
       " 'ccp_alpha': 0.0,\n",
       " 'class_weight': None,\n",
       " 'criterion': 'gini',\n",
       " 'max_depth': None,\n",
       " 'max_features': 'sqrt',\n",
       " 'max_leaf_nodes': None,\n",
       " 'max_samples': None,\n",
       " 'min_impurity_decrease': 0.0,\n",
       " 'min_samples_leaf': 1,\n",
       " 'min_samples_split': 2,\n",
       " 'min_weight_fraction_leaf': 0.0,\n",
       " 'n_estimators': 100,\n",
       " 'n_jobs': None,\n",
       " 'oob_score': False,\n",
       " 'random_state': None,\n",
       " 'verbose': 0,\n",
       " 'warm_start': False}"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.get_params()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "be3d177a",
   "metadata": {},
   "source": [
    "We're going to try and adjust:\n",
    "\n",
    "* `max_depth`\n",
    "* `max_features`\n",
    "* `min_sample_leaf`\n",
    "* `min_sample_split`\n",
    "* `n_estimator`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "74d92740",
   "metadata": {},
   "outputs": [],
   "source": [
    "def evaluate_preds(y_true, y_preds):\n",
    "    \"\"\"\n",
    "    Performs evaluation comparison on y_true labels vs. y_pred labels\n",
    "    \"\"\"\n",
    "    \n",
    "    accuracy= accuracy_score(y_true, y_preds)\n",
    "    precision= precision_score(y_true, y_preds)\n",
    "    recall=recall_score(y_true,y_preds)\n",
    "    f1=f1_score(y_true,y_preds)\n",
    "    metric_dict={\"accuracy\": round(accuracy,2),\n",
    "                 \"precision\": round(precision, 2),\n",
    "                 \"recall\" : round(recall, 2),\n",
    "                  \"f1\"     : round(f1,2)}\n",
    "    print(f\"Acc:{accuracy * 100:.2f}%\")\n",
    "    print(f\"Pricision: {precision:.2f}\")\n",
    "    print(f\"Recall: {recall:.2f}\")\n",
    "    print(f\"F1 score:{f1:.2f}\")\n",
    "    \n",
    "    return metric_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "b4b8fd18",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Acc:82.22%\n",
      "Pricision: 0.81\n",
      "Recall: 0.88\n",
      "F1 score:0.85\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'accuracy': 0.82, 'precision': 0.81, 'recall': 0.88, 'f1': 0.85}"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "np.random.seed(42)\n",
    "\n",
    "\n",
    "# Shuffle the data\n",
    "heart_disease_shuffled= heart_disease.sample(frac=1)\n",
    "\n",
    "# Split into x & Y\n",
    "x=heart_disease_shuffled.drop(\"target\", axis=1)\n",
    "y=heart_disease_shuffled[\"target\"]\n",
    "\n",
    "# Split the data into train, validation & test sets\n",
    "train_split=round(0.7 * len(heart_disease_shuffled)) # 70% of data\n",
    "valid_split=round(train_split + 0.15 * len(heart_disease_shuffled)) # 15% of data\n",
    "x_train, y_train=x[:train_split], y[:train_split]\n",
    "x_valid, y_valid=x[train_split:valid_split], y[train_split:valid_split]\n",
    "x_test, y_test= x[valid_split:], y[valid_split:]\n",
    "\n",
    "len(x_train), len(x_valid), len(x_test)\n",
    "\n",
    "clf=RandomForestClassifier()\n",
    "clf.fit(x_train, y_train)\n",
    "\n",
    "#Make baseline predictions\n",
    "y_preds=clf.predict(x_valid)\n",
    "\n",
    "# Evaluate the classifier on validation set\n",
    "\n",
    "baseline_metrics=evaluate_preds(y_valid, y_preds)\n",
    "baseline_metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "4ca03728",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'bootstrap': True,\n",
       " 'ccp_alpha': 0.0,\n",
       " 'class_weight': None,\n",
       " 'criterion': 'gini',\n",
       " 'max_depth': None,\n",
       " 'max_features': 'sqrt',\n",
       " 'max_leaf_nodes': None,\n",
       " 'max_samples': None,\n",
       " 'min_impurity_decrease': 0.0,\n",
       " 'min_samples_leaf': 1,\n",
       " 'min_samples_split': 2,\n",
       " 'min_weight_fraction_leaf': 0.0,\n",
       " 'n_estimators': 100,\n",
       " 'n_jobs': None,\n",
       " 'oob_score': False,\n",
       " 'random_state': None,\n",
       " 'verbose': 0,\n",
       " 'warm_start': False}"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.get_params()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "dbabf842",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Acc:82.22%\n",
      "Pricision: 0.84\n",
      "Recall: 0.84\n",
      "F1 score:0.84\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(42)\n",
    "\n",
    "# Create a second classifier with different hyperparameters\n",
    "\n",
    "clf_2=RandomForestClassifier(n_estimators=100)\n",
    "clf_2.fit(x_train, y_train)\n",
    "\n",
    "\n",
    "# Make prediction wtih different hyperparameters\n",
    "y_preds_2=clf_2.predict(x_valid)\n",
    "\n",
    "# Evalute the 2nd classifier\n",
    "clf_2_metrics= evaluate_preds(y_valid, y_preds_2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3f5089cd",
   "metadata": {},
   "source": [
    "## 5.2 Hyperparameter tuning with RandomizedSearchCV\n"
   ]
  },
  {
   "cell_type": "raw",
   "id": "6ac2e9b8",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "8d6134b8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 10 candidates, totalling 50 fits\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=12000; total time=  46.4s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=12000; total time=  46.9s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=12000; total time=  47.3s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=12000; total time=  47.2s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=12000; total time=  46.9s\n",
      "[CV] END max_depth=30, max_features=auto, min_samples_leaf=2, min_samples_split=4, n_estimators=100; total time=   0.3s\n",
      "[CV] END max_depth=30, max_features=auto, min_samples_leaf=2, min_samples_split=4, n_estimators=100; total time=   0.3s\n",
      "[CV] END max_depth=30, max_features=auto, min_samples_leaf=2, min_samples_split=4, n_estimators=100; total time=   0.3s\n",
      "[CV] END max_depth=30, max_features=auto, min_samples_leaf=2, min_samples_split=4, n_estimators=100; total time=   0.4s\n",
      "[CV] END max_depth=30, max_features=auto, min_samples_leaf=2, min_samples_split=4, n_estimators=100; total time=   0.4s\n",
      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=200; total time=   0.7s\n",
      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=200; total time=   0.8s\n",
      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=200; total time=   1.0s\n",
      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=200; total time=   0.7s\n",
      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=200; total time=   0.7s\n",
      "[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=6, n_estimators=100; total time=   0.5s\n",
      "[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=6, n_estimators=100; total time=   0.3s\n",
      "[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=6, n_estimators=100; total time=   0.3s\n",
      "[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=6, n_estimators=100; total time=   0.4s\n",
      "[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=6, n_estimators=100; total time=   0.5s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=1, min_samples_split=4, n_estimators=10; total time=   0.0s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=1, min_samples_split=4, n_estimators=10; total time=   0.0s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=1, min_samples_split=4, n_estimators=10; total time=   0.0s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=1, min_samples_split=4, n_estimators=10; total time=   0.0s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=1, min_samples_split=4, n_estimators=10; total time=   0.0s\n",
      "[CV] END max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=4, n_estimators=10; total time=   0.0s\n",
      "[CV] END max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=4, n_estimators=10; total time=   0.0s\n",
      "[CV] END max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=4, n_estimators=10; total time=   0.0s\n",
      "[CV] END max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=4, n_estimators=10; total time=   0.0s\n",
      "[CV] END max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=4, n_estimators=10; total time=   0.0s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=500; total time=   2.2s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=500; total time=   1.9s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=500; total time=   2.0s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=500; total time=   2.2s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=500; total time=   1.9s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=200; total time=   0.7s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=200; total time=   0.7s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=200; total time=   0.7s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=200; total time=   0.8s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=200; total time=   0.7s\n",
      "[CV] END max_depth=10, max_features=auto, min_samples_leaf=4, min_samples_split=4, n_estimators=200; total time=   0.7s\n",
      "[CV] END max_depth=10, max_features=auto, min_samples_leaf=4, min_samples_split=4, n_estimators=200; total time=   0.7s\n",
      "[CV] END max_depth=10, max_features=auto, min_samples_leaf=4, min_samples_split=4, n_estimators=200; total time=   0.7s\n",
      "[CV] END max_depth=10, max_features=auto, min_samples_leaf=4, min_samples_split=4, n_estimators=200; total time=   0.7s\n",
      "[CV] END max_depth=10, max_features=auto, min_samples_leaf=4, min_samples_split=4, n_estimators=200; total time=   0.7s\n",
      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=1000; total time=   3.6s\n",
      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=1000; total time=   3.6s\n",
      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=1000; total time=   3.6s\n",
      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=1000; total time=   3.6s\n",
      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=1000; total time=   3.6s\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "\n",
    "grid={\"n_estimators\": [10,100,200,500,1000,12000],\n",
    "      \"max_depth\": [None, 5,10,20,30],\n",
    "      \"max_features\" : [\"auto\",\"sqrt\"],\n",
    "      \"min_samples_split\" : [2, 4, 6],\n",
    "      \"min_samples_leaf\": [1, 2, 4]}\n",
    "\n",
    "np.random.seed(42)\n",
    "\n",
    "# Split into x & y\n",
    "x=heart_disease_shuffled.drop(\"target\", axis=1)\n",
    "y=heart_disease_shuffled[\"target\"]\n",
    "\n",
    "# Split into train and test sets\n",
    "x_train, x_test, y_train, y_test=train_test_split(x,y, test_size=0.2)\n",
    "\n",
    "# Instantiate RandomFirestClassifier\n",
    "clf=RandomForestClassifier(n_jobs=1)\n",
    "\n",
    "# Setup RandomizedSearchCV\n",
    "rs_clf=RandomizedSearchCV(estimator=clf,\n",
    "                          param_distributions=grid,\n",
    "                          n_iter=10, # number of models to try\n",
    "                          cv=5,\n",
    "                          verbose=2)\n",
    "\n",
    "# Fit the RandomizedSearchCV version of clf\n",
    "rs_clf.fit(x_train, y_train);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "bebe5a87",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'n_estimators': 100,\n",
       " 'min_samples_split': 6,\n",
       " 'min_samples_leaf': 1,\n",
       " 'max_features': 'auto',\n",
       " 'max_depth': 20}"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rs_clf.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "0ceaafca",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Acc:80.33%\n",
      "Pricision: 0.74\n",
      "Recall: 0.89\n",
      "F1 score:0.81\n"
     ]
    }
   ],
   "source": [
    "# Make prediction with the best hyperparameters\n",
    "rs_y_preds= rs_clf.predict(x_test)\n",
    "\n",
    "# Evaluate the predictions\n",
    "rs_metrics=evaluate_preds(y_test, rs_y_preds)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1dc530cf",
   "metadata": {},
   "source": [
    "## 5.3 HyperParameter tuning with GridSearchCv\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "595943dc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'n_estimators': [10, 100, 200, 500, 1000, 12000],\n",
       " 'max_depth': [None, 5, 10, 20, 30],\n",
       " 'max_features': ['auto', 'sqrt'],\n",
       " 'min_samples_split': [2, 4, 6],\n",
       " 'min_samples_leaf': [1, 2, 4]}"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "65dd03ed",
   "metadata": {},
   "outputs": [],
   "source": [
    "grid_2={'n_estimators': [ 100, 200, 500],\n",
    "        'max_depth': [20],\n",
    "        'max_features': ['auto', 'sqrt'],\n",
    "        'min_samples_split': [1],\n",
    "        'min_samples_leaf': [1, 2]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c4b6a134",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV, train_test_split\n",
    "\n",
    "np.random.seed(42)\n",
    "\n",
    "# Split into x & y\n",
    "x=heart_disease_shuffled.drop(\"target\", axis=1)\n",
    "y=heart_disease_shuffled[\"target\"]\n",
    "\n",
    "# Split into train and test sets\n",
    "x_train, x_test, y_train, y_test=train_test_split(x,y, test_size=0.2)\n",
    "\n",
    "# Instantiate RandomFirestClassifier\n",
    "clf=RandomForestClassifier(n_jobs=1)\n",
    "\n",
    "# SetupvgirdSearchCV\n",
    "gs_clf=GridSearchCV(estimator=clf,\n",
    "                          param_grid=grid_2,\n",
    "                          cv=5,\n",
    "                          verbose=2)\n",
    "\n",
    "# Fit the GridSearchCV version of clf\n",
    "gs_clf.fit(x_train, y_train);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0346caf0",
   "metadata": {},
   "outputs": [],
   "source": [
    "gs_clf.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "56523791",
   "metadata": {},
   "outputs": [],
   "source": [
    "gs_y_preds=gs_clf.predict(x_test)\n",
    "\n",
    "\n",
    "#Evaluate the predictions\n",
    "gs_metrics=evaluate_preds(y_test, gs_y_preds)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "180ddeaf",
   "metadata": {},
   "source": [
    "Let's compare our different models metrics\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "74d14357",
   "metadata": {},
   "outputs": [],
   "source": [
    "compare_metrics=pd.DataFrame({\"baseline\": baseline_metrics,\n",
    "                             \"clf_2\": clf_2_metrics,\n",
    "                             \"random search\" : rs_metrics,\n",
    "                             \"grid search\" : gs_metrics})\n",
    "                              \n",
    "\n",
    "compare_metrics.plot.bar(figsize=(10,8));"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f24c3398",
   "metadata": {},
   "source": [
    "## 6. Saving and loading trained machine learning models\n",
    "\n",
    "Two ways to save and load machine learning models:\n",
    "1. With Python's `pickle` module\n",
    "2.with the `joblib` module\n",
    "\n",
    "**Pickle**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "02caa330",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "\n",
    "# Save an extising model to file\n",
    "\n",
    "pickle.dump(gs_clf, open(\"gs_random_random_forest_model_1.pkl\", \"wb\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "798fbd64",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load a saved model\n",
    "\n",
    "loaded_pickle_model=pickle.load(open(\"gs_random_random_forest_model_1.pkl\", \"rb\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "04fc915d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make some presictions\n",
    "\n",
    "pickle_y_preds= loaded_pickle_model.predict(x_test)\n",
    "evaluate_preds(y_test,pickle_y_preds)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "12cff4c0",
   "metadata": {},
   "source": [
    "**Joblib**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4f77d814",
   "metadata": {},
   "outputs": [],
   "source": [
    "from joblib import dump, load\n",
    "\n",
    "# Save model to file\n",
    "dump(gs_clf, filename=\"gs_random_forest_model_1.joblib\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8ffb0f75",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import a saved joblib model \n",
    "loaded_joblib_model=load(filename=\"gs_random_forest_model_1.joblib\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0e414495",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make and evaluate joblib model \n",
    "\n",
    "joblib_y_preds=loaded_joblib_model.predict(x_test)\n",
    "evaluate_preds(y_test, joblib_y_preds)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "262672f6",
   "metadata": {},
   "source": [
    "## 7.Puting all together!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6813e271",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6a358c2e",
   "metadata": {},
   "outputs": [],
   "source": [
    "data=pd.read_csv(\"csv/car-sales-extended-missing-data.csv\")\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dc2895af",
   "metadata": {},
   "outputs": [],
   "source": [
    "data.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9772f21d",
   "metadata": {},
   "outputs": [],
   "source": [
    "data.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "34a19abe",
   "metadata": {},
   "source": [
    "Steps we want to do (all in one cell):\n",
    "1. Fill missing data\n",
    "2.Convert data to numbers\n",
    "3.Build a model on the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d5e85a8f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Getting data ready\n",
    "import pandas as pd\n",
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "\n",
    "\n",
    "# Modeling\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.model_selection import train_test_split, GridSearchCV\n",
    "\n",
    "# Setup random seed\n",
    "import numpy as np\n",
    "np.random.seed(42)\n",
    "\n",
    "# Import data and drop rows with missing labels\n",
    "data=pd.read_csv(\"csv/car-sales-extended-missing-data.csv\")\n",
    "data.dropna(subset=[\"Price\"], inplace=True)\n",
    "\n",
    "#Define different features and trasnformer pipeline\n",
    "categorical_features=[\"Make\", \"Colour\"]\n",
    "categorical_transformer=Pipeline(steps=[\n",
    "    (\"imputer\", SimpleImputer(strategy=\"constant\", fill_value=\"missing\")),\n",
    "    (\"onehot\", OneHotEncoder(handle_unknown=\"ignore\"))])\n",
    "\n",
    "door_feature=[\"Doors\"]\n",
    "door_transformer=Pipeline(steps=[\n",
    "    (\"imputer\", SimpleImputer(strategy=\"constant\", fill_value=4))\n",
    "])\n",
    "\n",
    "numerical_features=[\"Odometer (KM)\"]\n",
    "numerical_transformer=Pipeline(steps=[\n",
    "    (\"imputer\", SimpleImputer(strategy=\"mean\"))\n",
    "])\n",
    "\n",
    "# Setup the preprocessing steps(fill missing values, then convert to numbers)\n",
    "preprocessor= ColumnTransformer(\n",
    "                    transformers=[\n",
    "                        (\"cat\", categorical_transformer, categorical_features),\n",
    "                        (\"door\", door_transformer, door_feature),\n",
    "                        (\"num\" , numerical_transformer, numerical_features)\n",
    "                    ])\n",
    "\n",
    "# Creating a preprocessing and modelling pipeline\n",
    "model=Pipeline(steps=[(\"preprocessor\", preprocessor),\n",
    "                      (\"model\",RandomForestRegressor())])\n",
    "\n",
    "# Split data\n",
    "x=data.drop(\"Price\", axis=1)\n",
    "y=data[\"Price\"]\n",
    "x_train, x_test, y_train, y_test=train_test_split(x, y, test_size=0.2)\n",
    "\n",
    "# Fit and score the model\n",
    "model.fit(x_train, y_train)\n",
    "model.score(x_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "721c47e7",
   "metadata": {},
   "source": [
    "It's also possible to use `GridSearchCV` or `RandomizedSearchCV` with our Pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d6599ea5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Use GridSearchCv woth our regression Pipeline\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "pipe_grid={\n",
    "    \"preprocessor__num__imputer_strategy\":[\"mean\", \"median\"],\n",
    "    \"model__n_estimators\":[100,1000],\n",
    "    \"model__max_depth\":[None, 5],\n",
    "    \"model__max_features\": [\"auto\"],\n",
    "    \"model__min_samples_split\": [2,4]\n",
    "}\n",
    "\n",
    "gs_model=GridSearchCV(model, pipe_grid, cv=5, verbose=2)\n",
    "gs_model.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "af403fe9",
   "metadata": {},
   "outputs": [],
   "source": [
    "gs_model.score(x_test, y_test)"
   ]
  }
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