heroku_regression.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 260,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split  \n",
    "from sklearn.linear_model import LinearRegression\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn import metrics\n",
    "import numpy as np\n",
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import pickle\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 261,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Regression model\n",
    "#Now it is finally time to make our prediction. Linear regression will be used.\n",
    "\n",
    "df=pd.read_csv(\"heroku_for_regression.csv\")\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 2160x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "col = 'Zone'  # 'bar'\n",
    "n = 120  \n",
    "df1=df[df.groupby(col)[col].transform('count').ge(n)]\n",
    "carrier_count = df1['Zone'].value_counts()\n",
    "sns.set(style=\"darkgrid\")\n",
    "plt.figure(figsize=(30,10)) \n",
    "sns.barplot(carrier_count.index, carrier_count.values, alpha=1)\n",
    "plt.title('Frequency Distribution of Zones')\n",
    "plt.ylabel('Number of Occurrences', fontsize=12)\n",
    "plt.xlabel('Neighbourhood', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 262,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2 = df.copy()\n",
    "lb_make = LabelEncoder()\n",
    "df2['Zone'] = lb_make.fit_transform(df2['Zone'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 263,
   "metadata": {},
   "outputs": [],
   "source": [
    "df3 = df.copy()\n",
    "df3['Zonenc'] = lb_make.fit_transform(df3['Zone'])\n",
    "associazione= df3[['Zone','Zonenc']] \n",
    "\n",
    "nomi=associazione.Zone.unique()\n",
    "numero=associazione.Zonenc.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 264,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "X=df2.loc[:, df.columns != 'Price']\n",
    "y=df2['Price']\n",
    "#split in Test and training set\n",
    "X_train,X_test,Y_train,Y_test=train_test_split(X,y,test_size=1/3,random_state=0)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 248,
   "metadata": {},
   "outputs": [],
   "source": [
    "#linear regression\n",
    "linearRegressor = LinearRegression()\n",
    "linearRegressor.fit(X_train, Y_train)\n",
    "yPrediction = linearRegressor.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df3 = pd.DataFrame({'Actual': Y_test, 'Predicted': yPrediction})\n",
    "df3 = df3.head(25)\n",
    "df3\n",
    "\n",
    "df3.plot(kind='bar',figsize=(10,8))\n",
    "plt.grid(which='major', linestyle='-', linewidth='0.5', color='green')\n",
    "plt.grid(which='minor', linestyle=':', linewidth='0.5', color='black')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "metadata": {},
   "outputs": [],
   "source": [
    "filename = 'finalized_model.sav'\n",
    "pickle.dump(linearRegressor, open(filename, 'wb'))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 290,
   "metadata": {},
   "outputs": [],
   "source": [
    "loaded_model = pickle.load(open('finalized_model.sav', 'rb'))\n",
    "xx=np.array([2,80,1,7,160])\n",
    "xx=xx.reshape(1, -1)\n",
    "yPrediction = linearRegressor.predict(xx)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 292,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Other try but very poor result--> ignore\n",
    "\n",
    "from sklearn import svm\n",
    "clf = svm.SVC(gamma='auto')\n",
    "clf.fit(X_train, Y_train)\n",
    "a=clf.predict(X_test)\n",
    "df4 = pd.DataFrame({'Actual': Y_test, 'Predicted': a})\n",
    "df4 = df4.head(25)\n",
    "\n",
    "df4.plot(kind='bar',figsize=(10,8))\n",
    "plt.grid(which='major', linestyle='-', linewidth='0.5', color='green')\n",
    "plt.grid(which='minor', linestyle=':', linewidth='0.5', color='black')\n",
    "plt.show()"
   ]
  }
 ],
 "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": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}