Case Study - House Pricing.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import make_regression\n",
    "X,Y = make_regression(n_features=2, noise=10, n_samples=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7f94dc7ffe10>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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.xlabel('Feature - X')\n",
    "plt.ylabel('Target - Y')\n",
    "plt.scatter(X,Y,s=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1000,)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "lr = LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None,\n",
       "         normalize=False)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.fit(X[:-5],Y[:-5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([87.67988726, 65.65095185])"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.3965757598497561"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.intercept_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 24.04634029, -89.6787145 , -17.72120496, -28.40503389,\n",
       "        49.57480827])"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.predict(X[-5:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.55385326,  0.19914855],\n",
       "       [ 0.02960369, -0.43587681],\n",
       "       [ 0.92296907,  0.0818329 ],\n",
       "       [ 1.36378636,  0.91960906],\n",
       "       [-1.01986912, -0.74783077]])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 11.82219061, -89.42525167,  -4.95072224, -28.91193084,\n",
       "        39.78001041])"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y[-5:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "house_data = pd.read_csv('https://raw.githubusercontent.com/zekelabs/data-science-complete-tutorial/master/Data/house_rental_data.csv.txt', index_col='Unnamed: 0')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "house_data.rename(columns={'Living.Room':'Livingroom'}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>Sqft</th>\n",
       "      <th>Floor</th>\n",
       "      <th>TotalFloor</th>\n",
       "      <th>Bedroom</th>\n",
       "      <th>Livingroom</th>\n",
       "      <th>Bathroom</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1177.698</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>62000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2134.800</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>78000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1138.560</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>58000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1458.780</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>45000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>967.776</td>\n",
       "      <td>11</td>\n",
       "      <td>14</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>45000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Sqft  Floor  TotalFloor  Bedroom  Livingroom  Bathroom  Price\n",
       "1  1177.698      2           7        2           2         2  62000\n",
       "2  2134.800      5           7        4           2         2  78000\n",
       "3  1138.560      5           7        2           2         1  58000\n",
       "4  1458.780      2           7        3           2         2  45000\n",
       "5   967.776     11          14        3           2         2  45000"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "house_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "columns = house_data.columns.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "columns.remove('Price')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_data = house_data[columns]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "target_data = house_data.Price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "trainX,testX, trainY,testY = train_test_split(feature_data, target_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(483, 6)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainX.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(162, 6)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "testX.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "lr = LinearRegression(normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=True)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.fit(trainX,trainY)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([   36.39377254,   785.19438452,   495.56784421,  -999.01827554,\n",
       "       -5462.07383562,  2983.51532975])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>Sqft</th>\n",
       "      <th>Floor</th>\n",
       "      <th>TotalFloor</th>\n",
       "      <th>Bedroom</th>\n",
       "      <th>Livingroom</th>\n",
       "      <th>Bathroom</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>453</th>\n",
       "      <td>925.080</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>237</th>\n",
       "      <td>1757.652</td>\n",
       "      <td>4</td>\n",
       "      <td>16</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>2149.032</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>431</th>\n",
       "      <td>3187.968</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>639</th>\n",
       "      <td>1889.298</td>\n",
       "      <td>18</td>\n",
       "      <td>21</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Sqft  Floor  TotalFloor  Bedroom  Livingroom  Bathroom\n",
       "453   925.080      2           4        2           2         1\n",
       "237  1757.652      4          16        4           2         2\n",
       "125  2149.032      5           6        4           2         2\n",
       "431  3187.968      6           7        3           2         2\n",
       "639  1889.298     18          21        3           2         2"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "testX[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "453     36000\n",
       "237     55000\n",
       "125     80000\n",
       "431    168000\n",
       "639     70000\n",
       "Name: Price, dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "testY[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 29740.78380331,  68543.9014708 ,  78617.21210898, 118707.79307846,\n",
       "        87804.57493015])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.predict(testX[:5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import mean_squared_error, mean_absolute_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred = lr.predict(testX)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "17406.10760812217"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_absolute_error(y_pred=pred, y_true=testY)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import Ridge,Lasso"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "ridge = Ridge(alpha=1000)\n",
    "lasso = Lasso(alpha=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Lasso(alpha=1000, copy_X=True, fit_intercept=True, max_iter=1000,\n",
       "   normalize=False, positive=False, precompute=False, random_state=None,\n",
       "   selection='cyclic', tol=0.0001, warm_start=False)"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ridge.fit(trainX,trainY)\n",
    "lasso.fit(trainX,trainY)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred = ridge.predict(testX)\n",
    "pred = lasso.predict(testX)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "17303.821844570546"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_absolute_error(y_pred=pred, y_true=testY)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_iris"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "iris = load_iris()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "logreg = LogisticRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/awantik/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/home/awantik/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=100, multi_class='warn',\n",
       "          n_jobs=None, penalty='l2', random_state=None, solver='warn',\n",
       "          tol=0.0001, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "logreg.fit(iris.data, iris.target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[8.78030305e-01, 1.21958900e-01, 1.07949250e-05]])"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "logreg.predict_proba(iris.data[:1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0])"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.target[:1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.96"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "logreg.score(iris.data, iris.target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import PolynomialFeatures"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "pol = PolynomialFeatures(degree=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.,  1.,  2.,  1.,  2.,  4.],\n",
       "       [ 1.,  3.,  4.,  9., 12., 16.]])"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pol.fit_transform([[1,2],[3,4]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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  "kernelspec": {
   "display_name": "Python 3",
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
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