Classification/Modeling-KNN.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Modeling - KNN\n",
    "(Using the non-interacted features)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Importing the libraries and Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import accuracy_score, confusion_matrix\n",
    "import matplotlib.pyplot as plt\n",
    "import _pickle as pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(\"main_df.pkl\", \"rb\") as fp:\n",
    "    df = pickle.load(fp)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preparing the Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Scaling the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "scaler = StandardScaler()\n",
    "\n",
    "features_df = df.drop([\"Price\", \"Future % Return\", \"Decision\"], 1)\n",
    "\n",
    "scaled_df = pd.DataFrame(scaler.fit_transform(features_df), \n",
    "                               index=features_df.index, \n",
    "                               columns=features_df.columns)\n",
    "\n",
    "df = scaled_df.join(df.Decision)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train, test, split the DF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.drop([\"Decision\"], 1)\n",
    "y = df.Decision\n",
    "\n",
    "# Train, test, split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_confusion_matrix(y_true, y_pred, labels=[\"Sell\", \"Buy\", \"Hold\"], \n",
    "                          normalize=False, title=None, cmap=plt.cm.cividis):\n",
    "\n",
    "    cm = confusion_matrix(y_true, y_pred)\n",
    "    fig, ax = plt.subplots(figsize=(12,6))\n",
    "    im = ax.imshow(cm, interpolation='nearest', cmap=cmap)\n",
    "    ax.figure.colorbar(im, ax=ax)\n",
    "    # We want to show all ticks...\n",
    "    ax.set(xticks=np.arange(cm.shape[1]),\n",
    "           yticks=np.arange(cm.shape[0]),\n",
    "           # ... and label them with the respective list entries\n",
    "           xticklabels=labels, yticklabels=labels,\n",
    "           title=title,\n",
    "           ylabel='ACTUAL',\n",
    "           xlabel='PREDICTED')\n",
    "    # Rotate the tick labels and set their alignment.\n",
    "    plt.setp(ax.get_xticklabels(), rotation=45, ha=\"right\",\n",
    "             rotation_mode=\"anchor\")\n",
    "    # Loop over data dimensions and create text annotations.\n",
    "    fmt = '.2f' if normalize else 'd'\n",
    "    thresh = cm.max() / 2.\n",
    "    for i in range(cm.shape[0]):\n",
    "        for j in range(cm.shape[1]):\n",
    "            ax.text(j, i, format(cm[i, j], fmt),\n",
    "                    ha=\"center\", va=\"center\",\n",
    "                    color=\"crimson\" if cm[i, j] > thresh else \"crimson\",\n",
    "                    size=26)\n",
    "    ax.grid(False)\n",
    "    fig.tight_layout()\n",
    "    return ax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "def mod_results(clf, X_train, X_test, y_train, y_test):\n",
    "    \"\"\"\n",
    "    Displays the results of a fitted model with a Confusion Matrix\n",
    "    \"\"\"\n",
    "    # Getting predictions for the training set\n",
    "    train_preds = clf.predict(X_train)\n",
    "\n",
    "    # Getting predictions for the testing set\n",
    "    test_preds = clf.predict(X_test)\n",
    "\n",
    "    # Scores for both predictions\n",
    "    train_score = accuracy_score(y_train, train_preds)\n",
    "    test_score = accuracy_score(y_test, test_preds)\n",
    "\n",
    "    # Printing Results\n",
    "    print(f\"Training Accuracy: {train_score} \\t\\t Testing Accuracy: {test_score}\")\n",
    "    \n",
    "    # Displaying Confusion Matrix\n",
    "    plot_confusion_matrix(y_test, test_preds, title=\"Confusion Matrix\")\n",
    "    np.set_printoptions(precision=1)\n",
    "    \n",
    "    # Plot non-normalized confusion matrix\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### PCA"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Reducing the feature set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "\n",
    "# Instatiating\n",
    "pca = PCA()\n",
    "\n",
    "# Fitting PCA\n",
    "X_pca = pca.fit(X_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Determining the number of features to be reduced to."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1008x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number features: 23\tTotal Variance Explained: 0.9546620415216024\n"
     ]
    }
   ],
   "source": [
    "plt.style.use(\"bmh\")\n",
    "plt.figure(figsize=(14,4))\n",
    "plt.plot(range(1,X_train.shape[1]+1), X_pca.explained_variance_ratio_.cumsum())\n",
    "plt.show()\n",
    "\n",
    "total_explained_variance = X_pca.explained_variance_ratio_.cumsum()\n",
    "n_over_95 = len(total_explained_variance[total_explained_variance>=.95])\n",
    "n_to_reach_95 = X.shape[1] - n_over_95\n",
    "\n",
    "print(f\"Number features: {n_to_reach_95}\\tTotal Variance Explained: {total_explained_variance[n_to_reach_95]}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9407976180983177"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Implementing the reduced number of features\n",
    "pca = PCA(n_components=n_to_reach_95)\n",
    "\n",
    "X_pca_train = pca.fit_transform(X_train)\n",
    "X_pca_test = pca.transform(X_test)\n",
    "\n",
    "pca.explained_variance_ratio_.cumsum()[-1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## KNN Modeling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "           metric_params=None, n_jobs=None, n_neighbors=200, p=2,\n",
       "           weights='uniform')"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "# Instatiating the model classifier\n",
    "k_clf = KNeighborsClassifier(n_neighbors=200)\n",
    "\n",
    "# Fitting to the Data\n",
    "k_clf.fit(X_pca_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "K Nearest Neighbors\n",
      "Training Accuracy: 0.4514230232417016 \t\t Testing Accuracy: 0.4389222647005279\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Printing Results\n",
    "print(\"K Nearest Neighbors\")\n",
    "\n",
    "# Modeling and Confusion Matrix\n",
    "mod_results(k_clf, X_pca_train, X_pca_test, y_train, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## KNN - Results\n",
    "with PCA and scaled Data but with no interacted features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# K Nearest Neighbors\n",
    "# Training Accuracy: 0.4514230232417016 \t\t Testing Accuracy: 0.4389222647005279"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
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