Ml_scikit_learn_basics.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df = pd.read_csv('diamonds.csv', index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>carat</th>\n",
       "      <th>cut</th>\n",
       "      <th>color</th>\n",
       "      <th>clarity</th>\n",
       "      <th>depth</th>\n",
       "      <th>table</th>\n",
       "      <th>price</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>z</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.23</td>\n",
       "      <td>Ideal</td>\n",
       "      <td>E</td>\n",
       "      <td>SI2</td>\n",
       "      <td>61.5</td>\n",
       "      <td>55.0</td>\n",
       "      <td>326</td>\n",
       "      <td>3.95</td>\n",
       "      <td>3.98</td>\n",
       "      <td>2.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.21</td>\n",
       "      <td>Premium</td>\n",
       "      <td>E</td>\n",
       "      <td>SI1</td>\n",
       "      <td>59.8</td>\n",
       "      <td>61.0</td>\n",
       "      <td>326</td>\n",
       "      <td>3.89</td>\n",
       "      <td>3.84</td>\n",
       "      <td>2.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.23</td>\n",
       "      <td>Good</td>\n",
       "      <td>E</td>\n",
       "      <td>VS1</td>\n",
       "      <td>56.9</td>\n",
       "      <td>65.0</td>\n",
       "      <td>327</td>\n",
       "      <td>4.05</td>\n",
       "      <td>4.07</td>\n",
       "      <td>2.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.29</td>\n",
       "      <td>Premium</td>\n",
       "      <td>I</td>\n",
       "      <td>VS2</td>\n",
       "      <td>62.4</td>\n",
       "      <td>58.0</td>\n",
       "      <td>334</td>\n",
       "      <td>4.20</td>\n",
       "      <td>4.23</td>\n",
       "      <td>2.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.31</td>\n",
       "      <td>Good</td>\n",
       "      <td>J</td>\n",
       "      <td>SI2</td>\n",
       "      <td>63.3</td>\n",
       "      <td>58.0</td>\n",
       "      <td>335</td>\n",
       "      <td>4.34</td>\n",
       "      <td>4.35</td>\n",
       "      <td>2.75</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   carat      cut color clarity  depth  table  price     x     y     z\n",
       "1   0.23    Ideal     E     SI2   61.5   55.0    326  3.95  3.98  2.43\n",
       "2   0.21  Premium     E     SI1   59.8   61.0    326  3.89  3.84  2.31\n",
       "3   0.23     Good     E     VS1   56.9   65.0    327  4.05  4.07  2.31\n",
       "4   0.29  Premium     I     VS2   62.4   58.0    334  4.20  4.23  2.63\n",
       "5   0.31     Good     J     SI2   63.3   58.0    335  4.34  4.35  2.75"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Ideal' 'Premium' 'Good' 'Very Good' 'Fair']\n",
      "['SI2' 'SI1' 'VS1' 'VS2' 'VVS2' 'VVS1' 'I1' 'IF']\n"
     ]
    }
   ],
   "source": [
    "print(df['cut'].unique())\n",
    "print(df['clarity'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df['cut'].astype('category').cat.codes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "cut_class_dict = {'Fair':1,'Good':2,'Very Good':3,'Premium':4,'Ideal':5}\n",
    "clarity_dict = {\"I3\": 1, \"I2\": 2, \"I1\": 3, \"SI2\": 4, \"SI1\": 5, \"VS2\": 6, \"VS1\": 7, \"VVS2\": 8, \"VVS1\": 9, \"IF\": 10, \"FL\": 11}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "color_dict = {\"J\": 1,\"I\": 2,\"H\": 3,\"G\": 4,\"F\": 5,\"E\": 6,\"D\": 7}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['cut'] = df['cut'].map(cut_class_dict)\n",
    "df['clarity'] = df['clarity'].map(clarity_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['color'] = df['color'].map(color_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>carat</th>\n",
       "      <th>cut</th>\n",
       "      <th>color</th>\n",
       "      <th>clarity</th>\n",
       "      <th>depth</th>\n",
       "      <th>table</th>\n",
       "      <th>price</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>z</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.23</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>61.5</td>\n",
       "      <td>55.0</td>\n",
       "      <td>326</td>\n",
       "      <td>3.95</td>\n",
       "      <td>3.98</td>\n",
       "      <td>2.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.21</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>59.8</td>\n",
       "      <td>61.0</td>\n",
       "      <td>326</td>\n",
       "      <td>3.89</td>\n",
       "      <td>3.84</td>\n",
       "      <td>2.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.23</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>56.9</td>\n",
       "      <td>65.0</td>\n",
       "      <td>327</td>\n",
       "      <td>4.05</td>\n",
       "      <td>4.07</td>\n",
       "      <td>2.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.29</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>62.4</td>\n",
       "      <td>58.0</td>\n",
       "      <td>334</td>\n",
       "      <td>4.20</td>\n",
       "      <td>4.23</td>\n",
       "      <td>2.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.31</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>63.3</td>\n",
       "      <td>58.0</td>\n",
       "      <td>335</td>\n",
       "      <td>4.34</td>\n",
       "      <td>4.35</td>\n",
       "      <td>2.75</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "   carat  cut  color  clarity  depth  table  price     x     y     z\n",
       "1   0.23    5      6        4   61.5   55.0    326  3.95  3.98  2.43\n",
       "2   0.21    4      6        5   59.8   61.0    326  3.89  3.84  2.31\n",
       "3   0.23    2      6        7   56.9   65.0    327  4.05  4.07  2.31\n",
       "4   0.29    4      2        6   62.4   58.0    334  4.20  4.23  2.63\n",
       "5   0.31    2      1        4   63.3   58.0    335  4.34  4.35  2.75"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sklearn\n",
    "from sklearn.linear_model import SGDRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# to shuffle the data\n",
    "df = sklearn.utils.shuffle(df)\n",
    "\n",
    "# Creating training and test data\n",
    "\n",
    "X =  df.drop('price',axis=1)\n",
    "y = df['price']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# print(X.head(),y.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_size = 200\n",
    "\n",
    "X_train = X[:-test_size]\n",
    "X_test = X[-test_size:]\n",
    "\n",
    "y_train = y[:-test_size]\n",
    "y_test = y[-test_size:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Sweta\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:183: FutureWarning: max_iter and tol parameters have been added in SGDRegressor in 0.19. If max_iter is set but tol is left unset, the default value for tol in 0.19 and 0.20 will be None (which is equivalent to -infinity, so it has no effect) but will change in 0.21 to 1e-3. Specify tol to silence this warning.\n",
      "  FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "SGDRegressor(alpha=0.0001, average=False, early_stopping=False, epsilon=0.1,\n",
       "       eta0=0.01, fit_intercept=True, l1_ratio=0.15,\n",
       "       learning_rate='invscaling', loss='squared_loss', max_iter=1000,\n",
       "       n_iter=None, n_iter_no_change=5, penalty='l2', power_t=0.25,\n",
       "       random_state=None, shuffle=True, tol=None, validation_fraction=0.1,\n",
       "       verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf = SGDRegressor(max_iter=1000)\n",
    "\n",
    "clf.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-189508130.08061036\n"
     ]
    }
   ],
   "source": [
    "print(clf.score(X_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4167.337025247247 3581\n",
      "3922.2135026147594 4309\n",
      "782.4580274911832 648\n",
      "5746.787301470182 6819\n",
      "5116.8969171190065 4853\n",
      "1153.8518427125196 1200\n",
      "1437.5098083778933 1813\n",
      "725.7004610793133 969\n",
      "2877.0795695098873 2875\n",
      "6875.21870402041 8877\n"
     ]
    }
   ],
   "source": [
    "for X,y in list(zip(X_test,y_test))[:10]:\n",
    "    print(clf.predict([X])[0],y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Sweta\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py:196: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.6023175917447323\n",
      "model predicts 4167.337025247247, real value: 3581\n",
      "model predicts 3922.2135026147594, real value: 4309\n",
      "model predicts 782.4580274911832, real value: 648\n",
      "model predicts 5746.787301470182, real value: 6819\n",
      "model predicts 5116.8969171190065, real value: 4853\n",
      "model predicts 1153.8518427125196, real value: 1200\n",
      "model predicts 1437.5098083778933, real value: 1813\n",
      "model predicts 725.7004610793133, real value: 969\n",
      "model predicts 2877.0795695098873, real value: 2875\n",
      "model predicts 6875.21870402041, real value: 8877\n"
     ]
    }
   ],
   "source": [
    "import sklearn\n",
    "from sklearn import svm, preprocessing\n",
    "\n",
    "df = sklearn.utils.shuffle(df) \n",
    "\n",
    "X = df.drop(\"price\", axis=1).values\n",
    "X = preprocessing.scale(X)\n",
    "y = df[\"price\"].values\n",
    "\n",
    "test_size = 200\n",
    "\n",
    "X_train = X[:-test_size]\n",
    "y_train = y[:-test_size]\n",
    "\n",
    "X_test = X[-test_size:]\n",
    "y_test = y[-test_size:]\n",
    "\n",
    "clf = svm.SVR()\n",
    "\n",
    "clf.fit(X_train, y_train)\n",
    "print(clf.score(X_test, y_test))\n",
    "\n",
    "for X,y in list(zip(X_test, y_test))[:10]:\n",
    "    print(f\"model predicts {clf.predict([X])[0]}, real value: {y}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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