TestModelForExpense.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from datetime import date, timedelta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>date</th>\n",
       "      <th>desc</th>\n",
       "      <th>category</th>\n",
       "      <th>amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>01.06.2021.</td>\n",
       "      <td>food</td>\n",
       "      <td>food</td>\n",
       "      <td>100.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>02.06.2021.</td>\n",
       "      <td>medicines</td>\n",
       "      <td>meds</td>\n",
       "      <td>150.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>03.06.2021.</td>\n",
       "      <td>bank</td>\n",
       "      <td>bank</td>\n",
       "      <td>150.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>04.06.2021.</td>\n",
       "      <td>food</td>\n",
       "      <td>food</td>\n",
       "      <td>20.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>05.06.2021.</td>\n",
       "      <td>Emergency medicines</td>\n",
       "      <td>meds</td>\n",
       "      <td>50.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>10.06.2021.</td>\n",
       "      <td>bank</td>\n",
       "      <td>bank</td>\n",
       "      <td>100.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>10.06.2021.</td>\n",
       "      <td>food1</td>\n",
       "      <td>food</td>\n",
       "      <td>256.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>11.06.2021.</td>\n",
       "      <td>bank</td>\n",
       "      <td>bank</td>\n",
       "      <td>179.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>12.06.2021.</td>\n",
       "      <td>medicines</td>\n",
       "      <td>meds</td>\n",
       "      <td>500.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>14.06.2021.</td>\n",
       "      <td>Coffee and Breakfast</td>\n",
       "      <td>food</td>\n",
       "      <td>160.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          date                  desc category  amount\n",
       "0  01.06.2021.                  food     food  100.00\n",
       "1  02.06.2021.             medicines     meds  150.00\n",
       "2  03.06.2021.                  bank     bank  150.00\n",
       "3  04.06.2021.                  food     food   20.00\n",
       "4  05.06.2021.   Emergency medicines     meds   50.00\n",
       "5  10.06.2021.                  bank     bank  100.00\n",
       "6  10.06.2021.                 food1     food  256.45\n",
       "7  11.06.2021.                  bank     bank  179.05\n",
       "8  12.06.2021.             medicines     meds  500.00\n",
       "9  14.06.2021.  Coffee and Breakfast     food  160.00"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('download.csv')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def transform_row(r):\n",
    "    if len(r.date) == 6:\n",
    "        r.date += '2021.'\n",
    "    d = r.date[:-1].split('.')\n",
    "    r.date = date(*map(int, d[::-1]))\n",
    "    return r"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.apply(transform_row, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "daily_expenses = []\n",
    "all_dates = list(pd.date_range(min(df['date']), max(df['date']), freq='D'))\n",
    "categories = []\n",
    "for d in list(all_dates):\n",
    "    value = sum(df[df['date'] == d.date()]['amount'])\n",
    "    if value:\n",
    "        categories.append(df[df['date'] == d.date()]['category'].values[-1])\n",
    "        daily_expenses.append((d.date(), value))\n",
    "    else:\n",
    "        all_dates.remove(d)\n",
    "dates, sums = zip(*daily_expenses)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(datetime.date(2021, 6, 1), 100.0),\n",
       " (datetime.date(2021, 6, 2), 150.0),\n",
       " (datetime.date(2021, 6, 3), 150.0),\n",
       " (datetime.date(2021, 6, 4), 20.0),\n",
       " (datetime.date(2021, 6, 5), 50.0),\n",
       " (datetime.date(2021, 6, 10), 356.45),\n",
       " (datetime.date(2021, 6, 11), 179.05),\n",
       " (datetime.date(2021, 6, 12), 500.0),\n",
       " (datetime.date(2021, 6, 14), 160.0)]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "daily_expenses"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# encoding strings\n",
    "x = np.array([*zip(range(len(dates)), categories)])\n",
    "y = sums\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "from sklearn.compose import ColumnTransformer, make_column_transformer\n",
    "preprocess = make_column_transformer((OneHotEncoder(), [-1])).fit_transform(x)\n",
    "x = np.array([*zip(preprocess, x[:, 0])])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = x[:, 1:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([['0'],\n",
       "       ['1'],\n",
       "       ['2'],\n",
       "       ['3'],\n",
       "       ['4'],\n",
       "       ['5'],\n",
       "       ['6'],\n",
       "       ['7'],\n",
       "       ['8']], dtype=object)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# splitting into test set and training set\n",
    "from sklearn.model_selection import train_test_split as tts\n",
    "xtrain, xtest, ytrain, ytest = tts(x, y, test_size = 0.4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([['7'],\n",
       "       ['6'],\n",
       "       ['1'],\n",
       "       ['8']], dtype=object)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xtest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# fitting the regressor to our training set\n",
    "from sklearn.linear_model import LinearRegression\n",
    "regressor = LinearRegression()\n",
    "regressor.fit(xtrain, ytrain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# applying the regressor to our test set\n",
    "ypred = regressor.predict(xtest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([262.47905405, 232.19594595,  80.78040541, 292.76216216])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ypred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[500.0, 179.05, 150.0, 160.0]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ytest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plt.plot(ytest, color = 'green')\n",
    "plt.plot(ypred, color = 'navy')\n",
    "plt.ylabel('predicted amount')\n",
    "plt.xlabel('days in the dataset')\n",
    "plt.legend(['prediction'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "ypred1 = regressor.predict([[14]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([474.46081081])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ypred1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "#plt.plot(ytest, color = 'green')\n",
    "#plt.plot(ypred1, color = 'navy')\n",
    "#plt.ylabel('predicted amount')\n",
    "#plt.xlabel('days in the test set')\n",
    "#plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.04609708581947969"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import r2_score\n",
    "r2_score(ytest,ypred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}