├── 50_Startups.csv
├── Course Slides.pdf
├── Decision_tree.ipynb
├── K-means+algorithm+numpy&pandas+clustering.ipynb
├── KNN_Binary_Classification.ipynb
├── LICENSE
├── Movie_Id_Titles
├── MultipleLinearRegression.ipynb
├── README.md
├── Recommender+Systems+with+Python.ipynb
├── homeprices.csv
├── linear_regression_houseprice.ipynb
├── logistic_regression_Binary_Classification.ipynb
├── mall+customers+data.csv
├── mallCustomerData.txt
├── salaries.csv
├── u.data
└── user+data.csv


/50_Startups.csv:
--------------------------------------------------------------------------------
 1 | R&D Spend,Administration,Marketing Spend,State,Profit
 2 | 165349.2,136897.8,471784.1,New York,192261.83
 3 | 162597.7,151377.59,443898.53,California,191792.06
 4 | 153441.51,101145.55,407934.54,Florida,191050.39
 5 | 144372.41,118671.85,383199.62,New York,182901.99
 6 | 142107.34,91391.77,366168.42,Florida,166187.94
 7 | 131876.9,99814.71,362861.36,New York,156991.12
 8 | 134615.46,147198.87,127716.82,California,156122.51
 9 | 130298.13,145530.06,323876.68,Florida,155752.6
10 | 120542.52,148718.95,311613.29,New York,152211.77
11 | 123334.88,108679.17,304981.62,California,149759.96
12 | 101913.08,110594.11,229160.95,Florida,146121.95
13 | 100671.96,91790.61,249744.55,California,144259.4
14 | 93863.75,127320.38,249839.44,Florida,141585.52
15 | 91992.39,135495.07,252664.93,California,134307.35
16 | 119943.24,156547.42,256512.92,Florida,132602.65
17 | 114523.61,122616.84,261776.23,New York,129917.04
18 | 78013.11,121597.55,264346.06,California,126992.93
19 | 94657.16,145077.58,282574.31,New York,125370.37
20 | 91749.16,114175.79,294919.57,Florida,124266.9
21 | 86419.7,153514.11,0,New York,122776.86
22 | 76253.86,113867.3,298664.47,California,118474.03
23 | 78389.47,153773.43,299737.29,New York,111313.02
24 | 73994.56,122782.75,303319.26,Florida,110352.25
25 | 67532.53,105751.03,304768.73,Florida,108733.99
26 | 77044.01,99281.34,140574.81,New York,108552.04
27 | 64664.71,139553.16,137962.62,California,107404.34
28 | 75328.87,144135.98,134050.07,Florida,105733.54
29 | 72107.6,127864.55,353183.81,New York,105008.31
30 | 66051.52,182645.56,118148.2,Florida,103282.38
31 | 65605.48,153032.06,107138.38,New York,101004.64
32 | 61994.48,115641.28,91131.24,Florida,99937.59
33 | 61136.38,152701.92,88218.23,New York,97483.56
34 | 63408.86,129219.61,46085.25,California,97427.84
35 | 55493.95,103057.49,214634.81,Florida,96778.92
36 | 46426.07,157693.92,210797.67,California,96712.8
37 | 46014.02,85047.44,205517.64,New York,96479.51
38 | 28663.76,127056.21,201126.82,Florida,90708.19
39 | 44069.95,51283.14,197029.42,California,89949.14
40 | 20229.59,65947.93,185265.1,New York,81229.06
41 | 38558.51,82982.09,174999.3,California,81005.76
42 | 28754.33,118546.05,172795.67,California,78239.91
43 | 27892.92,84710.77,164470.71,Florida,77798.83
44 | 23640.93,96189.63,148001.11,California,71498.49
45 | 15505.73,127382.3,35534.17,New York,69758.98
46 | 22177.74,154806.14,28334.72,California,65200.33
47 | 1000.23,124153.04,1903.93,New York,64926.08
48 | 1315.46,115816.21,297114.46,Florida,49490.75
49 | 0,135426.92,0,California,42559.73
50 | 542.05,51743.15,0,New York,35673.41
51 | 0,116983.8,45173.06,California,14681.4
52 | 


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/Course Slides.pdf:
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https://raw.githubusercontent.com/PacktPublishing/Python-for-Machine-Learning---The-Complete-Beginner-s-Course/2816065b1edc1409f50b944691315452770c931b/Course Slides.pdf


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/Decision_tree.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "code",
  5 |    "execution_count": 1,
  6 |    "id": "e8de2b1d",
  7 |    "metadata": {},
  8 |    "outputs": [],
  9 |    "source": [
 10 |     "# Load libraries\n",
 11 |     "import pandas as pd\n",
 12 |     "from sklearn.tree import DecisionTreeClassifier # Import Decision Tree Classifier\n",
 13 |     "from sklearn.model_selection import train_test_split # Import train_test_split function\n",
 14 |     "from sklearn import metrics #Import scikit-learn metrics module for accuracy calculation\n"
 15 |    ]
 16 |   },
 17 |   {
 18 |    "cell_type": "code",
 19 |    "execution_count": 2,
 20 |    "id": "53c95a37",
 21 |    "metadata": {},
 22 |    "outputs": [],
 23 |    "source": [
 24 |     "#Working with the dataset here\n",
 25 |     "\n",
 26 |     "col_names = ['company', 'job', 'degree', 'salary_more_then_100k']# load dataset\n",
 27 |     "data = pd.read_csv(\"salaries.csv\", header=None, names=col_names)"
 28 |    ]
 29 |   },
 30 |   {
 31 |    "cell_type": "code",
 32 |    "execution_count": 4,
 33 |    "id": "3331ccec",
 34 |    "metadata": {},
 35 |    "outputs": [
 36 |     {
 37 |      "data": {
 38 |       "text/html": [
 39 |        "<div>\n",
 40 |        "<style scoped>\n",
 41 |        "    .dataframe tbody tr th:only-of-type {\n",
 42 |        "        vertical-align: middle;\n",
 43 |        "    }\n",
 44 |        "\n",
 45 |        "    .dataframe tbody tr th {\n",
 46 |        "        vertical-align: top;\n",
 47 |        "    }\n",
 48 |        "\n",
 49 |        "    .dataframe thead th {\n",
 50 |        "        text-align: right;\n",
 51 |        "    }\n",
 52 |        "</style>\n",
 53 |        "<table border=\"1\" class=\"dataframe\">\n",
 54 |        "  <thead>\n",
 55 |        "    <tr style=\"text-align: right;\">\n",
 56 |        "      <th></th>\n",
 57 |        "      <th>company</th>\n",
 58 |        "      <th>job</th>\n",
 59 |        "      <th>degree</th>\n",
 60 |        "      <th>salary_more_then_100k</th>\n",
 61 |        "    </tr>\n",
 62 |        "  </thead>\n",
 63 |        "  <tbody>\n",
 64 |        "    <tr>\n",
 65 |        "      <th>0</th>\n",
 66 |        "      <td>company</td>\n",
 67 |        "      <td>job</td>\n",
 68 |        "      <td>degree</td>\n",
 69 |        "      <td>salary_more_then_100k</td>\n",
 70 |        "    </tr>\n",
 71 |        "    <tr>\n",
 72 |        "      <th>1</th>\n",
 73 |        "      <td>google</td>\n",
 74 |        "      <td>sale exective</td>\n",
 75 |        "      <td>bachelaors</td>\n",
 76 |        "      <td>0</td>\n",
 77 |        "    </tr>\n",
 78 |        "    <tr>\n",
 79 |        "      <th>2</th>\n",
 80 |        "      <td>google</td>\n",
 81 |        "      <td>sale exective</td>\n",
 82 |        "      <td>masters</td>\n",
 83 |        "      <td>0</td>\n",
 84 |        "    </tr>\n",
 85 |        "    <tr>\n",
 86 |        "      <th>3</th>\n",
 87 |        "      <td>google</td>\n",
 88 |        "      <td>business manager</td>\n",
 89 |        "      <td>bachelaors</td>\n",
 90 |        "      <td>1</td>\n",
 91 |        "    </tr>\n",
 92 |        "    <tr>\n",
 93 |        "      <th>4</th>\n",
 94 |        "      <td>google</td>\n",
 95 |        "      <td>business manager</td>\n",
 96 |        "      <td>masters</td>\n",
 97 |        "      <td>1</td>\n",
 98 |        "    </tr>\n",
 99 |        "    <tr>\n",
100 |        "      <th>5</th>\n",
101 |        "      <td>google</td>\n",
102 |        "      <td>computer programmer</td>\n",
103 |        "      <td>bachelaors</td>\n",
104 |        "      <td>0</td>\n",
105 |        "    </tr>\n",
106 |        "    <tr>\n",
107 |        "      <th>6</th>\n",
108 |        "      <td>google</td>\n",
109 |        "      <td>computer programmer</td>\n",
110 |        "      <td>masters</td>\n",
111 |        "      <td>1</td>\n",
112 |        "    </tr>\n",
113 |        "    <tr>\n",
114 |        "      <th>7</th>\n",
115 |        "      <td>abc pharma</td>\n",
116 |        "      <td>sale exective</td>\n",
117 |        "      <td>masters</td>\n",
118 |        "      <td>0</td>\n",
119 |        "    </tr>\n",
120 |        "    <tr>\n",
121 |        "      <th>8</th>\n",
122 |        "      <td>abc pharma</td>\n",
123 |        "      <td>computer programmer</td>\n",
124 |        "      <td>bachelaors</td>\n",
125 |        "      <td>0</td>\n",
126 |        "    </tr>\n",
127 |        "    <tr>\n",
128 |        "      <th>9</th>\n",
129 |        "      <td>abc pharma</td>\n",
130 |        "      <td>business manager</td>\n",
131 |        "      <td>bachelaors</td>\n",
132 |        "      <td>0</td>\n",
133 |        "    </tr>\n",
134 |        "  </tbody>\n",
135 |        "</table>\n",
136 |        "</div>"
137 |       ],
138 |       "text/plain": [
139 |        "      company                  job      degree  salary_more_then_100k\n",
140 |        "0     company                  job      degree  salary_more_then_100k\n",
141 |        "1      google        sale exective  bachelaors                      0\n",
142 |        "2      google        sale exective     masters                      0\n",
143 |        "3      google     business manager  bachelaors                      1\n",
144 |        "4      google     business manager     masters                      1\n",
145 |        "5      google  computer programmer  bachelaors                      0\n",
146 |        "6      google  computer programmer     masters                      1\n",
147 |        "7  abc pharma        sale exective     masters                      0\n",
148 |        "8  abc pharma  computer programmer  bachelaors                      0\n",
149 |        "9  abc pharma     business manager  bachelaors                      0"
150 |       ]
151 |      },
152 |      "execution_count": 4,
153 |      "metadata": {},
154 |      "output_type": "execute_result"
155 |     }
156 |    ],
157 |    "source": [
158 |     "data.head(10)"
159 |    ]
160 |   },
161 |   {
162 |    "cell_type": "code",
163 |    "execution_count": 64,
164 |    "id": "cd20f4ec",
165 |    "metadata": {},
166 |    "outputs": [
167 |     {
168 |      "name": "stdout",
169 |      "output_type": "stream",
170 |      "text": [
171 |       "   company  job  degree  salary_more_then_100k\n",
172 |       "0        1    2       1  salary_more_then_100k\n",
173 |       "1        3    3       0                      0\n",
174 |       "2        3    3       2                      0\n",
175 |       "3        3    0       0                      1\n",
176 |       "4        3    0       2                      1\n"
177 |      ]
178 |     }
179 |    ],
180 |    "source": [
181 |     "# Import label encoder \n",
182 |     "from sklearn import preprocessing\n",
183 |     "# label_encoder object knows how to understand word labels. \n",
184 |     "label_encoder = preprocessing.LabelEncoder()\n",
185 |     "# Encode labels in column \n",
186 |     "data['company']= label_encoder.fit_transform(data['company'])\n",
187 |     "data['job']= label_encoder.fit_transform(data['job'])\n",
188 |     "data['degree']= label_encoder.fit_transform(data['degree'])\n",
189 |     "print(data.head())\n"
190 |    ]
191 |   },
192 |   {
193 |    "cell_type": "code",
194 |    "execution_count": 65,
195 |    "id": "2308290f",
196 |    "metadata": {},
197 |    "outputs": [
198 |     {
199 |      "name": "stdout",
200 |      "output_type": "stream",
201 |      "text": [
202 |       "    company  job  degree\n",
203 |       "0         1    2       1\n",
204 |       "1         3    3       0\n",
205 |       "2         3    3       2\n",
206 |       "3         3    0       0\n",
207 |       "4         3    0       2\n",
208 |       "5         3    1       0\n",
209 |       "6         3    1       2\n",
210 |       "7         0    3       2\n",
211 |       "8         0    1       0\n",
212 |       "9         0    0       0\n",
213 |       "10        0    0       2\n",
214 |       "11        2    3       0\n",
215 |       "12        2    3       2\n",
216 |       "13        2    0       0\n",
217 |       "14        2    0       2\n",
218 |       "15        2    1       0\n",
219 |       "16        2    1       2\n",
220 |       "0     salary_more_then_100k\n",
221 |       "1                         0\n",
222 |       "2                         0\n",
223 |       "3                         1\n",
224 |       "4                         1\n",
225 |       "5                         0\n",
226 |       "6                         1\n",
227 |       "7                         0\n",
228 |       "8                         0\n",
229 |       "9                         0\n",
230 |       "10                        1\n",
231 |       "11                        1\n",
232 |       "12                        1\n",
233 |       "13                        1\n",
234 |       "14                        1\n",
235 |       "15                        1\n",
236 |       "16                        1\n",
237 |       "Name: salary_more_then_100k, dtype: object\n"
238 |      ]
239 |     }
240 |    ],
241 |    "source": [
242 |     "#Split the dataset in features and target variable\n",
243 |     "feature_cols = ['company','job','degree']\n",
244 |     "X = data[feature_cols]\n",
245 |     "y = data['salary_more_then_100k']\n",
246 |     "# X = data.values[1:,:3]\n",
247 |     "# y = data.values[1:,3] # 1:,3 one means we are not using the header\n",
248 |     "print(X)\n",
249 |     "print(y)"
250 |    ]
251 |   },
252 |   {
253 |    "cell_type": "code",
254 |    "execution_count": null,
255 |    "id": "6d0b5f1b",
256 |    "metadata": {},
257 |    "outputs": [],
258 |    "source": []
259 |   },
260 |   {
261 |    "cell_type": "code",
262 |    "execution_count": 66,
263 |    "id": "d6fa9ecb",
264 |    "metadata": {},
265 |    "outputs": [],
266 |    "source": [
267 |     "# data = pd.get_dummies(data,columns=['company','job','degree','salary_more_then_100k'])\n",
268 |     "# print(data)"
269 |    ]
270 |   },
271 |   {
272 |    "cell_type": "code",
273 |    "execution_count": 67,
274 |    "id": "b09580a7",
275 |    "metadata": {},
276 |    "outputs": [],
277 |    "source": [
278 |     "X_train, X_test,y_train,y_test = train_test_split(X,y,test_size=0.3,random_state=100)\n"
279 |    ]
280 |   },
281 |   {
282 |    "cell_type": "code",
283 |    "execution_count": 73,
284 |    "id": "27908c92",
285 |    "metadata": {},
286 |    "outputs": [
287 |     {
288 |      "name": "stdout",
289 |      "output_type": "stream",
290 |      "text": [
291 |       "Accuracy: 0.3333333333333333\n"
292 |      ]
293 |     }
294 |    ],
295 |    "source": [
296 |     "# Create Decision Tree classifier object using entropy\n",
297 |     "clf_entropy = DecisionTreeClassifier(criterion=\"entropy\", max_depth=3)\n",
298 |     "# Train Decision Tree Classifier\n",
299 |     "clf_entropy = clf_entropy.fit(X_train,y_train)\n",
300 |     "#Predict the response for test dataset\n",
301 |     "y_pred = clf_entropy.predict(X_test)\n",
302 |     "\n",
303 |     "\n",
304 |     "# # Creating the classifier object using gini index\n",
305 |     "# clf_gini = DecisionTreeClassifier(criterion = \"gini\",random_state = 100,max_depth=3, min_samples_leaf=5)\n",
306 |     "# # Performing training\n",
307 |     "# clf_gini.fit(X_train, y_train)\n",
308 |     "# y_pred = clf_gini.predict(X_test)\n",
309 |     "\n",
310 |     "print(\"Accuracy:\", metrics.accuracy_score(y_test,y_pred))\n"
311 |    ]
312 |   },
313 |   {
314 |    "cell_type": "code",
315 |    "execution_count": 75,
316 |    "id": "cc803539",
317 |    "metadata": {},
318 |    "outputs": [
319 |     {
320 |      "name": "stdout",
321 |      "output_type": "stream",
322 |      "text": [
323 |       "Collecting graphviz\n",
324 |       "  Downloading graphviz-0.18-py3-none-any.whl (38 kB)\n",
325 |       "Installing collected packages: graphviz\n",
326 |       "Successfully installed graphviz-0.18\n"
327 |      ]
328 |     }
329 |    ],
330 |    "source": [
331 |     "import sys \n",
332 |     "!{sys.executable} -m pip install graphviz"
333 |    ]
334 |   },
335 |   {
336 |    "cell_type": "code",
337 |    "execution_count": 76,
338 |    "id": "277f2449",
339 |    "metadata": {},
340 |    "outputs": [
341 |     {
342 |      "name": "stdout",
343 |      "output_type": "stream",
344 |      "text": [
345 |       "Collecting pydotplus\n",
346 |       "  Downloading pydotplus-2.0.2.tar.gz (278 kB)\n",
347 |       "Requirement already satisfied: pyparsing>=2.0.1 in c:\\users\\jawad\\anaconda3\\lib\\site-packages (from pydotplus) (2.4.7)\n",
348 |       "Building wheels for collected packages: pydotplus\n",
349 |       "  Building wheel for pydotplus (setup.py): started\n",
350 |       "  Building wheel for pydotplus (setup.py): finished with status 'done'\n",
351 |       "  Created wheel for pydotplus: filename=pydotplus-2.0.2-py3-none-any.whl size=24566 sha256=8337f121af41726ad19cab3b0280ef43a5a63e078e1b028da2cef1c1206d904a\n",
352 |       "  Stored in directory: c:\\users\\jawad\\appdata\\local\\pip\\cache\\wheels\\fe\\cd\\78\\a7e873cc049759194f8271f780640cf96b35e5a48bef0e2f36\n",
353 |       "Successfully built pydotplus\n",
354 |       "Installing collected packages: pydotplus\n",
355 |       "Successfully installed pydotplus-2.0.2\n"
356 |      ]
357 |     }
358 |    ],
359 |    "source": [
360 |     "import sys \n",
361 |     "!{sys.executable} -m pip install pydotplus"
362 |    ]
363 |   },
364 |   {
365 |    "cell_type": "code",
366 |    "execution_count": null,
367 |    "id": "f516121e",
368 |    "metadata": {},
369 |    "outputs": [],
370 |    "source": [
371 |     "import sys \n",
372 |     "!{sys.executable} -m pip install --upgrade scikit-learn==0.20.3"
373 |    ]
374 |   },
375 |   {
376 |    "cell_type": "code",
377 |    "execution_count": 77,
378 |    "id": "1b902069",
379 |    "metadata": {},
380 |    "outputs": [
381 |     {
382 |      "ename": "ModuleNotFoundError",
383 |      "evalue": "No module named 'sklearn.externals.six'",
384 |      "output_type": "error",
385 |      "traceback": [
386 |       "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
387 |       "\u001b[1;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
388 |       "\u001b[1;32m<ipython-input-77-ec4cd8f60f6b>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0msklearn\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtree\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mexport_graphviz\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[1;32mfrom\u001b[0m \u001b[0msklearn\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mexternals\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msix\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mStringIO\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      3\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mIPython\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdisplay\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mImage\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mpydotplus\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[0mdot_data\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mStringIO\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
389 |       "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'sklearn.externals.six'"
390 |      ]
391 |     }
392 |    ],
393 |    "source": [
394 |     "from sklearn.tree import export_graphviz\n",
395 |     "from sklearn.externals.six import StringIO  \n",
396 |     "from IPython.display import Image  \n",
397 |     "import pydotplus\n",
398 |     "dot_data = StringIO()\n",
399 |     "export_graphviz(clf, out_file=dot_data,  \n",
400 |     "                filled=True, rounded=True,\n",
401 |     "                special_characters=True,feature_names = feature_cols,class_names=['0','1'])\n",
402 |     "graph = pydotplus.graph_from_dot_data(dot_data.getvalue())  \n",
403 |     "graph.write_png('tree.png')\n",
404 |     "Image(graph.create_png())"
405 |    ]
406 |   },
407 |   {
408 |    "cell_type": "code",
409 |    "execution_count": null,
410 |    "id": "ff5d0a84",
411 |    "metadata": {},
412 |    "outputs": [],
413 |    "source": []
414 |   },
415 |   {
416 |    "cell_type": "code",
417 |    "execution_count": null,
418 |    "id": "2c85a8dc",
419 |    "metadata": {},
420 |    "outputs": [],
421 |    "source": []
422 |   }
423 |  ],
424 |  "metadata": {
425 |   "kernelspec": {
426 |    "display_name": "Python 3 (ipykernel)",
427 |    "language": "python",
428 |    "name": "python3"
429 |   },
430 |   "language_info": {
431 |    "codemirror_mode": {
432 |     "name": "ipython",
433 |     "version": 3
434 |    },
435 |    "file_extension": ".py",
436 |    "mimetype": "text/x-python",
437 |    "name": "python",
438 |    "nbconvert_exporter": "python",
439 |    "pygments_lexer": "ipython3",
440 |    "version": "3.8.3"
441 |   }
442 |  },
443 |  "nbformat": 4,
444 |  "nbformat_minor": 5
445 | }
446 | 


--------------------------------------------------------------------------------
/KNN_Binary_Classification.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "code",
  5 |    "execution_count": 1,
  6 |    "id": "56ab457f",
  7 |    "metadata": {},
  8 |    "outputs": [],
  9 |    "source": [
 10 |     "#Importing the libraries\n",
 11 |     "import numpy as np\n",
 12 |     "import matplotlib.pyplot as plt\n",
 13 |     "import pandas as pd\n",
 14 |     "from sklearn.metrics import confusion_matrix"
 15 |    ]
 16 |   },
 17 |   {
 18 |    "cell_type": "code",
 19 |    "execution_count": 2,
 20 |    "id": "2438c0eb",
 21 |    "metadata": {},
 22 |    "outputs": [],
 23 |    "source": [
 24 |     "# Importing the dataset\n",
 25 |     "dataset = pd.read_csv('user data.csv')\n",
 26 |     "X = dataset.iloc[:, 2:4].values\n",
 27 |     "y = dataset.iloc[:, 4].values"
 28 |    ]
 29 |   },
 30 |   {
 31 |    "cell_type": "code",
 32 |    "execution_count": 7,
 33 |    "id": "14eebe92",
 34 |    "metadata": {},
 35 |    "outputs": [
 36 |     {
 37 |      "name": "stdout",
 38 |      "output_type": "stream",
 39 |      "text": [
 40 |       "[[    44  39000]\n",
 41 |       " [    32 120000]\n",
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263 |       " [    55 125000]\n",
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265 |       " [    41  71000]\n",
266 |       " [    30  62000]\n",
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269 |       " [    58  47000]\n",
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274 |       " [    49  89000]\n",
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280 |       " [    21  72000]\n",
281 |       " [    38  71000]\n",
282 |       " [    39 106000]\n",
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286 |       " [    54 108000]\n",
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288 |       " [    39 134000]\n",
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299 |       " [    39  73000]\n",
300 |       " [    41  79000]\n",
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302 |       " [    41  30000]\n",
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305 |       " [    25  22000]\n",
306 |       " [    28  37000]\n",
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310 |       " [    56 104000]\n",
311 |       " [    40  57000]\n",
312 |       " [    42 108000]\n",
313 |       " [    20  23000]\n",
314 |       " [    40  65000]\n",
315 |       " [    47  20000]\n",
316 |       " [    18  86000]\n",
317 |       " [    35  79000]\n",
318 |       " [    57  33000]\n",
319 |       " [    34  72000]\n",
320 |       " [    49  39000]\n",
321 |       " [    27  31000]\n",
322 |       " [    19  70000]\n",
323 |       " [    39  79000]\n",
324 |       " [    26  81000]\n",
325 |       " [    25  80000]\n",
326 |       " [    28  85000]\n",
327 |       " [    55  39000]\n",
328 |       " [    50  88000]\n",
329 |       " [    49  88000]\n",
330 |       " [    52 150000]\n",
331 |       " [    35  65000]\n",
332 |       " [    42  54000]\n",
333 |       " [    34  43000]\n",
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335 |       " [    48  30000]\n",
336 |       " [    29  43000]\n",
337 |       " [    36  52000]\n",
338 |       " [    27  54000]\n",
339 |       " [    26 118000]]\n",
340 |       "[[    30  87000]\n",
341 |       " [    38  50000]\n",
342 |       " [    35  75000]\n",
343 |       " [    30  79000]\n",
344 |       " [    35  50000]\n",
345 |       " [    27  20000]\n",
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347 |       " [    36 144000]\n",
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350 |       " [    30  49000]\n",
351 |       " [    28  55000]\n",
352 |       " [    37  55000]\n",
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354 |       " [    20  86000]\n",
355 |       " [    32 117000]\n",
356 |       " [    37  77000]\n",
357 |       " [    19  85000]\n",
358 |       " [    55 130000]\n",
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360 |       " [    35  47000]\n",
361 |       " [    47 144000]\n",
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365 |       " [    49 141000]\n",
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367 |       " [    29  80000]\n",
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369 |       " [    32  86000]\n",
370 |       " [    21  88000]\n",
371 |       " [    37  79000]\n",
372 |       " [    57  60000]\n",
373 |       " [    37  53000]\n",
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375 |       " [    18  52000]\n",
376 |       " [    22  81000]\n",
377 |       " [    34  43000]\n",
378 |       " [    31  34000]\n",
379 |       " [    49  36000]\n",
380 |       " [    27  88000]\n",
381 |       " [    41  52000]\n",
382 |       " [    27  84000]\n",
383 |       " [    35  20000]\n",
384 |       " [    43 112000]\n",
385 |       " [    27  58000]\n",
386 |       " [    37  80000]\n",
387 |       " [    52  90000]\n",
388 |       " [    26  30000]\n",
389 |       " [    49  86000]\n",
390 |       " [    57 122000]\n",
391 |       " [    34  25000]\n",
392 |       " [    35  57000]\n",
393 |       " [    34 115000]\n",
394 |       " [    59  88000]\n",
395 |       " [    45  32000]\n",
396 |       " [    29  83000]\n",
397 |       " [    26  80000]\n",
398 |       " [    49  28000]\n",
399 |       " [    23  20000]\n",
400 |       " [    32  18000]\n",
401 |       " [    60  42000]\n",
402 |       " [    19  76000]\n",
403 |       " [    36  99000]\n",
404 |       " [    19  26000]\n",
405 |       " [    60  83000]\n",
406 |       " [    24  89000]\n",
407 |       " [    27  58000]\n",
408 |       " [    40  47000]\n",
409 |       " [    42  70000]\n",
410 |       " [    32 150000]\n",
411 |       " [    35  77000]\n",
412 |       " [    22  63000]\n",
413 |       " [    45  22000]\n",
414 |       " [    27  89000]\n",
415 |       " [    18  82000]\n",
416 |       " [    42  79000]\n",
417 |       " [    40  60000]\n",
418 |       " [    53  34000]\n",
419 |       " [    47 107000]\n",
420 |       " [    58 144000]\n",
421 |       " [    59  83000]\n",
422 |       " [    24  55000]\n",
423 |       " [    26  35000]\n",
424 |       " [    58  38000]\n",
425 |       " [    42  80000]\n",
426 |       " [    40  75000]\n",
427 |       " [    59 130000]\n",
428 |       " [    46  41000]\n",
429 |       " [    41  60000]\n",
430 |       " [    42  64000]\n",
431 |       " [    37 146000]\n",
432 |       " [    23  48000]\n",
433 |       " [    25  33000]\n",
434 |       " [    24  84000]\n",
435 |       " [    27  96000]\n",
436 |       " [    23  63000]\n",
437 |       " [    48  33000]\n",
438 |       " [    48  90000]\n",
439 |       " [    42 104000]]\n"
440 |      ]
441 |     }
442 |    ],
443 |    "source": [
444 |     "#Training and Testing Data (divide the data into two part)\n",
445 |     "from sklearn.model_selection import train_test_split\n",
446 |     "X_train, X_test, y_train, y_test =train_test_split(X,y,test_size= 0.25, random_state=0)\n",
447 |     "\n",
448 |     "print(X_train)"
449 |    ]
450 |   },
451 |   {
452 |    "cell_type": "code",
453 |    "execution_count": 9,
454 |    "id": "952c4e44",
455 |    "metadata": {},
456 |    "outputs": [
457 |     {
458 |      "name": "stdout",
459 |      "output_type": "stream",
460 |      "text": [
461 |       "[[ 0.58164944 -0.88670699]\n",
462 |       " [-0.60673761  1.46173768]\n",
463 |       " [-0.01254409 -0.5677824 ]\n",
464 |       " [-0.60673761  1.89663484]\n",
465 |       " [ 1.37390747 -1.40858358]\n",
466 |       " [ 1.47293972  0.99784738]\n",
467 |       " [ 0.08648817 -0.79972756]\n",
468 |       " [-0.01254409 -0.24885782]\n",
469 |       " [-0.21060859 -0.5677824 ]\n",
470 |       " [-0.21060859 -0.19087153]\n",
471 |       " [-0.30964085 -1.29261101]\n",
472 |       " [-0.30964085 -0.5677824 ]\n",
473 |       " [ 0.38358493  0.09905991]\n",
474 |       " [ 0.8787462  -0.59677555]\n",
475 |       " [ 2.06713324 -1.17663843]\n",
476 |       " [ 1.07681071 -0.13288524]\n",
477 |       " [ 0.68068169  1.78066227]\n",
478 |       " [-0.70576986  0.56295021]\n",
479 |       " [ 0.77971394  0.35999821]\n",
480 |       " [ 0.8787462  -0.53878926]\n",
481 |       " [-1.20093113 -1.58254245]\n",
482 |       " [ 2.1661655   0.93986109]\n",
483 |       " [-0.01254409  1.22979253]\n",
484 |       " [ 0.18552042  1.08482681]\n",
485 |       " [ 0.38358493 -0.48080297]\n",
486 |       " [-0.30964085 -0.30684411]\n",
487 |       " [ 0.97777845 -0.8287207 ]\n",
488 |       " [ 0.97777845  1.8676417 ]\n",
489 |       " [-0.01254409  1.25878567]\n",
490 |       " [-0.90383437  2.27354572]\n",
491 |       " [-1.20093113 -1.58254245]\n",
492 |       " [ 2.1661655  -0.79972756]\n",
493 |       " [-1.39899564 -1.46656987]\n",
494 |       " [ 0.38358493  2.30253886]\n",
495 |       " [ 0.77971394  0.76590222]\n",
496 |       " [-1.00286662 -0.30684411]\n",
497 |       " [ 0.08648817  0.76590222]\n",
498 |       " [-1.00286662  0.56295021]\n",
499 |       " [ 0.28455268  0.07006676]\n",
500 |       " [ 0.68068169 -1.26361786]\n",
501 |       " [-0.50770535 -0.01691267]\n",
502 |       " [-1.79512465  0.35999821]\n",
503 |       " [-0.70576986  0.12805305]\n",
504 |       " [ 0.38358493  0.30201192]\n",
505 |       " [-0.30964085  0.07006676]\n",
506 |       " [-0.50770535  2.30253886]\n",
507 |       " [ 0.18552042  0.04107362]\n",
508 |       " [ 1.27487521  2.21555943]\n",
509 |       " [ 0.77971394  0.27301877]\n",
510 |       " [-0.30964085  0.1570462 ]\n",
511 |       " [-0.01254409 -0.53878926]\n",
512 |       " [-0.21060859  0.1570462 ]\n",
513 |       " [-0.11157634  0.24402563]\n",
514 |       " [-0.01254409 -0.24885782]\n",
515 |       " [ 2.1661655   1.11381995]\n",
516 |       " [-1.79512465  0.35999821]\n",
517 |       " [ 1.86906873  0.12805305]\n",
518 |       " [ 0.38358493 -0.13288524]\n",
519 |       " [-1.20093113  0.30201192]\n",
520 |       " [ 0.77971394  1.37475825]\n",
521 |       " [-0.30964085 -0.24885782]\n",
522 |       " [-1.6960924  -0.04590581]\n",
523 |       " [-1.00286662 -0.74174127]\n",
524 |       " [ 0.28455268  0.50496393]\n",
525 |       " [-0.11157634 -1.06066585]\n",
526 |       " [-1.10189888  0.59194336]\n",
527 |       " [ 0.08648817 -0.79972756]\n",
528 |       " [-1.00286662  1.54871711]\n",
529 |       " [-0.70576986  1.40375139]\n",
530 |       " [-1.29996338  0.50496393]\n",
531 |       " [-0.30964085  0.04107362]\n",
532 |       " [-0.11157634  0.01208048]\n",
533 |       " [-0.30964085 -0.88670699]\n",
534 |       " [ 0.8787462  -1.3505973 ]\n",
535 |       " [-0.30964085  2.24455257]\n",
536 |       " [ 0.97777845  1.98361427]\n",
537 |       " [-1.20093113  0.47597078]\n",
538 |       " [-1.29996338  0.27301877]\n",
539 |       " [ 1.37390747  1.98361427]\n",
540 |       " [ 1.27487521 -1.3505973 ]\n",
541 |       " [-0.30964085 -0.27785096]\n",
542 |       " [-0.50770535  1.25878567]\n",
543 |       " [-0.80480212  1.08482681]\n",
544 |       " [ 0.97777845 -1.06066585]\n",
545 |       " [ 0.28455268  0.30201192]\n",
546 |       " [ 0.97777845  0.76590222]\n",
547 |       " [-0.70576986 -1.49556302]\n",
548 |       " [-0.70576986  0.04107362]\n",
549 |       " [ 0.48261718  1.72267598]\n",
550 |       " [ 2.06713324  0.18603934]\n",
551 |       " [-1.99318916 -0.74174127]\n",
552 |       " [-0.21060859  1.40375139]\n",
553 |       " [ 0.38358493  0.59194336]\n",
554 |       " [ 0.8787462  -1.14764529]\n",
555 |       " [-1.20093113 -0.77073441]\n",
556 |       " [ 0.18552042  0.24402563]\n",
557 |       " [ 0.77971394 -0.30684411]\n",
558 |       " [ 2.06713324 -0.79972756]\n",
559 |       " [ 0.77971394  0.12805305]\n",
560 |       " [-0.30964085  0.6209365 ]\n",
561 |       " [-1.00286662 -0.30684411]\n",
562 |       " [ 0.18552042 -0.3648304 ]\n",
563 |       " [ 2.06713324  2.12857999]\n",
564 |       " [ 1.86906873 -1.26361786]\n",
565 |       " [ 1.37390747 -0.91570013]\n",
566 |       " [ 0.8787462   1.25878567]\n",
567 |       " [ 1.47293972  2.12857999]\n",
568 |       " [-0.30964085 -1.23462472]\n",
569 |       " [ 1.96810099  0.91086794]\n",
570 |       " [ 0.68068169 -0.71274813]\n",
571 |       " [-1.49802789  0.35999821]\n",
572 |       " [ 0.77971394 -1.3505973 ]\n",
573 |       " [ 0.38358493 -0.13288524]\n",
574 |       " [-1.00286662  0.41798449]\n",
575 |       " [-0.01254409 -0.30684411]\n",
576 |       " [-1.20093113  0.41798449]\n",
577 |       " [-0.90383437 -1.20563157]\n",
578 |       " [-0.11157634  0.04107362]\n",
579 |       " [-1.59706014 -0.42281668]\n",
580 |       " [ 0.97777845 -1.00267957]\n",
581 |       " [ 1.07681071 -1.20563157]\n",
582 |       " [-0.01254409 -0.13288524]\n",
583 |       " [-1.10189888 -1.52455616]\n",
584 |       " [ 0.77971394 -1.20563157]\n",
585 |       " [ 0.97777845  2.07059371]\n",
586 |       " [-1.20093113 -1.52455616]\n",
587 |       " [-0.30964085  0.79489537]\n",
588 |       " [ 0.08648817 -0.30684411]\n",
589 |       " [-1.39899564 -1.23462472]\n",
590 |       " [-0.60673761 -1.49556302]\n",
591 |       " [ 0.77971394  0.53395707]\n",
592 |       " [-0.30964085 -0.33583725]\n",
593 |       " [ 1.77003648 -0.27785096]\n",
594 |       " [ 0.8787462  -1.03167271]\n",
595 |       " [ 0.18552042  0.07006676]\n",
596 |       " [-0.60673761  0.8818748 ]\n",
597 |       " [-1.89415691 -1.40858358]\n",
598 |       " [-1.29996338  0.59194336]\n",
599 |       " [-0.30964085  0.53395707]\n",
600 |       " [-1.00286662 -1.089659  ]\n",
601 |       " [ 1.17584296 -1.43757673]\n",
602 |       " [ 0.18552042 -0.30684411]\n",
603 |       " [ 1.17584296 -0.74174127]\n",
604 |       " [-0.30964085  0.07006676]\n",
605 |       " [ 0.18552042  2.09958685]\n",
606 |       " [ 0.77971394 -1.089659  ]\n",
607 |       " [ 0.08648817  0.04107362]\n",
608 |       " [-1.79512465  0.12805305]\n",
609 |       " [-0.90383437  0.1570462 ]\n",
610 |       " [-0.70576986  0.18603934]\n",
611 |       " [ 0.8787462  -1.29261101]\n",
612 |       " [ 0.18552042 -0.24885782]\n",
613 |       " [-0.4086731   1.22979253]\n",
614 |       " [-0.01254409  0.30201192]\n",
615 |       " [ 0.38358493  0.1570462 ]\n",
616 |       " [ 0.8787462  -0.65476184]\n",
617 |       " [ 0.08648817  0.1570462 ]\n",
618 |       " [-1.89415691 -1.29261101]\n",
619 |       " [-0.11157634  0.30201192]\n",
620 |       " [-0.21060859 -0.27785096]\n",
621 |       " [ 0.28455268 -0.50979612]\n",
622 |       " [-0.21060859  1.6067034 ]\n",
623 |       " [ 0.97777845 -1.17663843]\n",
624 |       " [-0.21060859  1.63569655]\n",
625 |       " [ 1.27487521  1.8676417 ]\n",
626 |       " [-1.10189888 -0.3648304 ]\n",
627 |       " [-0.01254409  0.04107362]\n",
628 |       " [ 0.08648817 -0.24885782]\n",
629 |       " [-1.59706014 -1.23462472]\n",
630 |       " [-0.50770535 -0.27785096]\n",
631 |       " [ 0.97777845  0.12805305]\n",
632 |       " [ 1.96810099 -1.3505973 ]\n",
633 |       " [ 1.47293972  0.07006676]\n",
634 |       " [-0.60673761  1.37475825]\n",
635 |       " [ 1.57197197  0.01208048]\n",
636 |       " [-0.80480212  0.30201192]\n",
637 |       " [ 1.96810099  0.73690908]\n",
638 |       " [-1.20093113 -0.50979612]\n",
639 |       " [ 0.68068169  0.27301877]\n",
640 |       " [-1.39899564 -0.42281668]\n",
641 |       " [ 0.18552042  0.1570462 ]\n",
642 |       " [-0.50770535 -1.20563157]\n",
643 |       " [ 0.58164944  2.01260742]\n",
644 |       " [-1.59706014 -1.49556302]\n",
645 |       " [-0.50770535 -0.53878926]\n",
646 |       " [ 0.48261718  1.83864855]\n",
647 |       " [-1.39899564 -1.089659  ]\n",
648 |       " [ 0.77971394 -1.37959044]\n",
649 |       " [-0.30964085 -0.42281668]\n",
650 |       " [ 1.57197197  0.99784738]\n",
651 |       " [ 0.97777845  1.43274454]\n",
652 |       " [-0.30964085 -0.48080297]\n",
653 |       " [-0.11157634  2.15757314]\n",
654 |       " [-1.49802789 -0.1038921 ]\n",
655 |       " [-0.11157634  1.95462113]\n",
656 |       " [-0.70576986 -0.33583725]\n",
657 |       " [-0.50770535 -0.8287207 ]\n",
658 |       " [ 0.68068169 -1.37959044]\n",
659 |       " [-0.80480212 -1.58254245]\n",
660 |       " [-1.89415691 -1.46656987]\n",
661 |       " [ 1.07681071  0.12805305]\n",
662 |       " [ 0.08648817  1.51972397]\n",
663 |       " [-0.30964085  0.09905991]\n",
664 |       " [ 0.08648817  0.04107362]\n",
665 |       " [-1.39899564 -1.3505973 ]\n",
666 |       " [ 0.28455268  0.07006676]\n",
667 |       " [-0.90383437  0.38899135]\n",
668 |       " [ 1.57197197 -1.26361786]\n",
669 |       " [-0.30964085 -0.74174127]\n",
670 |       " [-0.11157634  0.1570462 ]\n",
671 |       " [-0.90383437 -0.65476184]\n",
672 |       " [-0.70576986 -0.04590581]\n",
673 |       " [ 0.38358493 -0.45180983]\n",
674 |       " [-0.80480212  1.89663484]\n",
675 |       " [ 1.37390747  1.28777882]\n",
676 |       " [ 1.17584296 -0.97368642]\n",
677 |       " [ 1.77003648  1.83864855]\n",
678 |       " [-0.90383437 -0.24885782]\n",
679 |       " [-0.80480212  0.56295021]\n",
680 |       " [-1.20093113 -1.5535493 ]\n",
681 |       " [-0.50770535 -1.11865214]\n",
682 |       " [ 0.28455268  0.07006676]\n",
683 |       " [-0.21060859 -1.06066585]\n",
684 |       " [ 1.67100423  1.6067034 ]\n",
685 |       " [ 0.97777845  1.78066227]\n",
686 |       " [ 0.28455268  0.04107362]\n",
687 |       " [-0.80480212 -0.21986468]\n",
688 |       " [-0.11157634  0.07006676]\n",
689 |       " [ 0.28455268 -0.19087153]\n",
690 |       " [ 1.96810099 -0.65476184]\n",
691 |       " [-0.80480212  1.3457651 ]\n",
692 |       " [-1.79512465 -0.59677555]\n",
693 |       " [-0.11157634  0.12805305]\n",
694 |       " [ 0.28455268 -0.30684411]\n",
695 |       " [ 1.07681071  0.56295021]\n",
696 |       " [-1.00286662  0.27301877]\n",
697 |       " [ 1.47293972  0.35999821]\n",
698 |       " [ 0.18552042 -0.3648304 ]\n",
699 |       " [ 2.1661655  -1.03167271]\n",
700 |       " [-0.30964085  1.11381995]\n",
701 |       " [-1.6960924   0.07006676]\n",
702 |       " [-0.01254409  0.04107362]\n",
703 |       " [ 0.08648817  1.05583366]\n",
704 |       " [-0.11157634 -0.3648304 ]\n",
705 |       " [-1.20093113  0.07006676]\n",
706 |       " [-0.30964085 -1.3505973 ]\n",
707 |       " [ 1.57197197  1.11381995]\n",
708 |       " [-0.80480212 -1.52455616]\n",
709 |       " [ 0.08648817  1.8676417 ]\n",
710 |       " [-0.90383437 -0.77073441]\n",
711 |       " [-0.50770535 -0.77073441]\n",
712 |       " [-0.30964085 -0.91570013]\n",
713 |       " [ 0.28455268 -0.71274813]\n",
714 |       " [ 0.28455268  0.07006676]\n",
715 |       " [ 0.08648817  1.8676417 ]\n",
716 |       " [-1.10189888  1.95462113]\n",
717 |       " [-1.6960924  -1.5535493 ]\n",
718 |       " [-1.20093113 -1.089659  ]\n",
719 |       " [-0.70576986 -0.1038921 ]\n",
720 |       " [ 0.08648817  0.09905991]\n",
721 |       " [ 0.28455268  0.27301877]\n",
722 |       " [ 0.8787462  -0.5677824 ]\n",
723 |       " [ 0.28455268 -1.14764529]\n",
724 |       " [-0.11157634  0.67892279]\n",
725 |       " [ 2.1661655  -0.68375498]\n",
726 |       " [-1.29996338 -1.37959044]\n",
727 |       " [-1.00286662 -0.94469328]\n",
728 |       " [-0.01254409 -0.42281668]\n",
729 |       " [-0.21060859 -0.45180983]\n",
730 |       " [-1.79512465 -0.97368642]\n",
731 |       " [ 1.77003648  0.99784738]\n",
732 |       " [ 0.18552042 -0.3648304 ]\n",
733 |       " [ 0.38358493  1.11381995]\n",
734 |       " [-1.79512465 -1.3505973 ]\n",
735 |       " [ 0.18552042 -0.13288524]\n",
736 |       " [ 0.8787462  -1.43757673]\n",
737 |       " [-1.99318916  0.47597078]\n",
738 |       " [-0.30964085  0.27301877]\n",
739 |       " [ 1.86906873 -1.06066585]\n",
740 |       " [-0.4086731   0.07006676]\n",
741 |       " [ 1.07681071 -0.88670699]\n",
742 |       " [-1.10189888 -1.11865214]\n",
743 |       " [-1.89415691  0.01208048]\n",
744 |       " [ 0.08648817  0.27301877]\n",
745 |       " [-1.20093113  0.33100506]\n",
746 |       " [-1.29996338  0.30201192]\n",
747 |       " [-1.00286662  0.44697764]\n",
748 |       " [ 1.67100423 -0.88670699]\n",
749 |       " [ 1.17584296  0.53395707]\n",
750 |       " [ 1.07681071  0.53395707]\n",
751 |       " [ 1.37390747  2.331532  ]\n",
752 |       " [-0.30964085 -0.13288524]\n",
753 |       " [ 0.38358493 -0.45180983]\n",
754 |       " [-0.4086731  -0.77073441]\n",
755 |       " [-0.11157634 -0.50979612]\n",
756 |       " [ 0.97777845 -1.14764529]\n",
757 |       " [-0.90383437 -0.77073441]\n",
758 |       " [-0.21060859 -0.50979612]\n",
759 |       " [-1.10189888 -0.45180983]\n",
760 |       " [-1.20093113  1.40375139]]\n"
761 |      ]
762 |     }
763 |    ],
764 |    "source": [
765 |     "from sklearn.preprocessing import StandardScaler\n",
766 |     "sc_X = StandardScaler()\n",
767 |     "X_train = sc_X.fit_transform(X_train)\n",
768 |     "X_test = sc_X.fit_transform(X_test)\n",
769 |     "\n",
770 |     "print(X_train)"
771 |    ]
772 |   },
773 |   {
774 |    "cell_type": "code",
775 |    "execution_count": 5,
776 |    "id": "6ae4bddc",
777 |    "metadata": {},
778 |    "outputs": [
779 |     {
780 |      "data": {
781 |       "text/plain": [
782 |        "KNeighborsClassifier()"
783 |       ]
784 |      },
785 |      "execution_count": 5,
786 |      "metadata": {},
787 |      "output_type": "execute_result"
788 |     }
789 |    ],
790 |    "source": [
791 |     "from sklearn.neighbors import KNeighborsClassifier\n",
792 |     "classifer=KNeighborsClassifier(n_neighbors=5, metric=\"minkowski\", p=2)\n",
793 |     "classifer.fit(X_train,y_train)"
794 |    ]
795 |   },
796 |   {
797 |    "cell_type": "code",
798 |    "execution_count": 6,
799 |    "id": "23fd4aec",
800 |    "metadata": {
801 |     "scrolled": true
802 |    },
803 |    "outputs": [
804 |     {
805 |      "name": "stdout",
806 |      "output_type": "stream",
807 |      "text": [
808 |       "[[64  3]\n",
809 |       " [ 4 29]]\n"
810 |      ]
811 |     }
812 |    ],
813 |    "source": [
814 |     "y_pred = classifer.predict(X_test)\n",
815 |     "\n",
816 |     "# Making the Confusion Matrix\n",
817 |     "cm = confusion_matrix(y_pred, y_test)\n",
818 |     "print(cm)"
819 |    ]
820 |   },
821 |   {
822 |    "cell_type": "code",
823 |    "execution_count": null,
824 |    "id": "ab42b07a",
825 |    "metadata": {},
826 |    "outputs": [],
827 |    "source": []
828 |   },
829 |   {
830 |    "cell_type": "code",
831 |    "execution_count": null,
832 |    "id": "84f25d32",
833 |    "metadata": {},
834 |    "outputs": [],
835 |    "source": []
836 |   }
837 |  ],
838 |  "metadata": {
839 |   "kernelspec": {
840 |    "display_name": "Python 3",
841 |    "language": "python",
842 |    "name": "python3"
843 |   },
844 |   "language_info": {
845 |    "codemirror_mode": {
846 |     "name": "ipython",
847 |     "version": 3
848 |    },
849 |    "file_extension": ".py",
850 |    "mimetype": "text/x-python",
851 |    "name": "python",
852 |    "nbconvert_exporter": "python",
853 |    "pygments_lexer": "ipython3",
854 |    "version": "3.8.8"
855 |   }
856 |  },
857 |  "nbformat": 4,
858 |  "nbformat_minor": 5
859 | }
860 | 


--------------------------------------------------------------------------------
/LICENSE:
--------------------------------------------------------------------------------
 1 | MIT License
 2 | 
 3 | Copyright (c) 2022 Packt
 4 | 
 5 | Permission is hereby granted, free of charge, to any person obtaining a copy
 6 | of this software and associated documentation files (the "Software"), to deal
 7 | in the Software without restriction, including without limitation the rights
 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
 9 | copies of the Software, and to permit persons to whom the Software is
10 | furnished to do so, subject to the following conditions:
11 | 
12 | The above copyright notice and this permission notice shall be included in all
13 | copies or substantial portions of the Software.
14 | 
15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
21 | SOFTWARE.
22 | 


--------------------------------------------------------------------------------
/Movie_Id_Titles:
--------------------------------------------------------------------------------
   1 | item_id,title
   2 | 1,Toy Story (1995)
   3 | 2,GoldenEye (1995)
   4 | 3,Four Rooms (1995)
   5 | 4,Get Shorty (1995)
   6 | 5,Copycat (1995)
   7 | 6,Shanghai Triad (Yao a yao yao dao waipo qiao) (1995)
   8 | 7,Twelve Monkeys (1995)
   9 | 8,Babe (1995)
  10 | 9,Dead Man Walking (1995)
  11 | 10,Richard III (1995)
  12 | 11,Seven (Se7en) (1995)
  13 | 12,"Usual Suspects, The (1995)"
  14 | 13,Mighty Aphrodite (1995)
  15 | 14,"Postino, Il (1994)"
  16 | 15,Mr. Holland's Opus (1995)
  17 | 16,French Twist (Gazon maudit) (1995)
  18 | 17,From Dusk Till Dawn (1996)
  19 | 18,"White Balloon, The (1995)"
  20 | 19,Antonia's Line (1995)
  21 | 20,Angels and Insects (1995)
  22 | 21,Muppet Treasure Island (1996)
  23 | 22,Braveheart (1995)
  24 | 23,Taxi Driver (1976)
  25 | 24,Rumble in the Bronx (1995)
  26 | 25,"Birdcage, The (1996)"
  27 | 26,"Brothers McMullen, The (1995)"
  28 | 27,Bad Boys (1995)
  29 | 28,Apollo 13 (1995)
  30 | 29,Batman Forever (1995)
  31 | 30,Belle de jour (1967)
  32 | 31,Crimson Tide (1995)
  33 | 32,Crumb (1994)
  34 | 33,Desperado (1995)
  35 | 34,"Doom Generation, The (1995)"
  36 | 35,Free Willy 2: The Adventure Home (1995)
  37 | 36,Mad Love (1995)
  38 | 37,Nadja (1994)
  39 | 38,"Net, The (1995)"
  40 | 39,Strange Days (1995)
  41 | 40,"To Wong Foo, Thanks for Everything! Julie Newmar (1995)"
  42 | 41,Billy Madison (1995)
  43 | 42,Clerks (1994)
  44 | 43,Disclosure (1994)
  45 | 44,Dolores Claiborne (1994)
  46 | 45,Eat Drink Man Woman (1994)
  47 | 46,Exotica (1994)
  48 | 47,Ed Wood (1994)
  49 | 48,Hoop Dreams (1994)
  50 | 49,I.Q. (1994)
  51 | 50,Star Wars (1977)
  52 | 51,Legends of the Fall (1994)
  53 | 52,"Madness of King George, The (1994)"
  54 | 53,Natural Born Killers (1994)
  55 | 54,Outbreak (1995)
  56 | 55,"Professional, The (1994)"
  57 | 56,Pulp Fiction (1994)
  58 | 57,Priest (1994)
  59 | 58,Quiz Show (1994)
  60 | 59,Three Colors: Red (1994)
  61 | 60,Three Colors: Blue (1993)
  62 | 61,Three Colors: White (1994)
  63 | 62,Stargate (1994)
  64 | 63,"Santa Clause, The (1994)"
  65 | 64,"Shawshank Redemption, The (1994)"
  66 | 65,What's Eating Gilbert Grape (1993)
  67 | 66,While You Were Sleeping (1995)
  68 | 67,Ace Ventura: Pet Detective (1994)
  69 | 68,"Crow, The (1994)"
  70 | 69,Forrest Gump (1994)
  71 | 70,Four Weddings and a Funeral (1994)
  72 | 71,"Lion King, The (1994)"
  73 | 72,"Mask, The (1994)"
  74 | 73,Maverick (1994)
  75 | 74,Faster Pussycat! Kill! Kill! (1965)
  76 | 75,Brother Minister: The Assassination of Malcolm X (1994)
  77 | 76,Carlito's Way (1993)
  78 | 77,"Firm, The (1993)"
  79 | 78,Free Willy (1993)
  80 | 79,"Fugitive, The (1993)"
  81 | 80,Hot Shots! Part Deux (1993)
  82 | 81,"Hudsucker Proxy, The (1994)"
  83 | 82,Jurassic Park (1993)
  84 | 83,Much Ado About Nothing (1993)
  85 | 84,Robert A. Heinlein's The Puppet Masters (1994)
  86 | 85,"Ref, The (1994)"
  87 | 86,"Remains of the Day, The (1993)"
  88 | 87,Searching for Bobby Fischer (1993)
  89 | 88,Sleepless in Seattle (1993)
  90 | 89,Blade Runner (1982)
  91 | 90,So I Married an Axe Murderer (1993)
  92 | 91,"Nightmare Before Christmas, The (1993)"
  93 | 92,True Romance (1993)
  94 | 93,Welcome to the Dollhouse (1995)
  95 | 94,Home Alone (1990)
  96 | 95,Aladdin (1992)
  97 | 96,Terminator 2: Judgment Day (1991)
  98 | 97,Dances with Wolves (1990)
  99 | 98,"Silence of the Lambs, The (1991)"
 100 | 99,Snow White and the Seven Dwarfs (1937)
 101 | 100,Fargo (1996)
 102 | 101,Heavy Metal (1981)
 103 | 102,"Aristocats, The (1970)"
 104 | 103,All Dogs Go to Heaven 2 (1996)
 105 | 104,Theodore Rex (1995)
 106 | 105,Sgt. Bilko (1996)
 107 | 106,Diabolique (1996)
 108 | 107,Moll Flanders (1996)
 109 | 108,Kids in the Hall: Brain Candy (1996)
 110 | 109,Mystery Science Theater 3000: The Movie (1996)
 111 | 110,Operation Dumbo Drop (1995)
 112 | 111,"Truth About Cats & Dogs, The (1996)"
 113 | 112,Flipper (1996)
 114 | 113,"Horseman on the Roof, The (Hussard sur le toit, Le) (1995)"
 115 | 114,Wallace & Gromit: The Best of Aardman Animation (1996)
 116 | 115,"Haunted World of Edward D. Wood Jr., The (1995)"
 117 | 116,Cold Comfort Farm (1995)
 118 | 117,"Rock, The (1996)"
 119 | 118,Twister (1996)
 120 | 119,Maya Lin: A Strong Clear Vision (1994)
 121 | 120,Striptease (1996)
 122 | 121,Independence Day (ID4) (1996)
 123 | 122,"Cable Guy, The (1996)"
 124 | 123,"Frighteners, The (1996)"
 125 | 124,Lone Star (1996)
 126 | 125,Phenomenon (1996)
 127 | 126,"Spitfire Grill, The (1996)"
 128 | 127,"Godfather, The (1972)"
 129 | 128,Supercop (1992)
 130 | 129,Bound (1996)
 131 | 130,Kansas City (1996)
 132 | 131,Breakfast at Tiffany's (1961)
 133 | 132,"Wizard of Oz, The (1939)"
 134 | 133,Gone with the Wind (1939)
 135 | 134,Citizen Kane (1941)
 136 | 135,2001: A Space Odyssey (1968)
 137 | 136,Mr. Smith Goes to Washington (1939)
 138 | 137,Big Night (1996)
 139 | 138,D3: The Mighty Ducks (1996)
 140 | 139,"Love Bug, The (1969)"
 141 | 140,Homeward Bound: The Incredible Journey (1993)
 142 | 141,"20,000 Leagues Under the Sea (1954)"
 143 | 142,Bedknobs and Broomsticks (1971)
 144 | 143,"Sound of Music, The (1965)"
 145 | 144,Die Hard (1988)
 146 | 145,"Lawnmower Man, The (1992)"
 147 | 146,Unhook the Stars (1996)
 148 | 147,"Long Kiss Goodnight, The (1996)"
 149 | 148,"Ghost and the Darkness, The (1996)"
 150 | 149,Jude (1996)
 151 | 150,Swingers (1996)
 152 | 151,Willy Wonka and the Chocolate Factory (1971)
 153 | 152,Sleeper (1973)
 154 | 153,"Fish Called Wanda, A (1988)"
 155 | 154,Monty Python's Life of Brian (1979)
 156 | 155,Dirty Dancing (1987)
 157 | 156,Reservoir Dogs (1992)
 158 | 157,Platoon (1986)
 159 | 158,Weekend at Bernie's (1989)
 160 | 159,Basic Instinct (1992)
 161 | 160,Glengarry Glen Ross (1992)
 162 | 161,Top Gun (1986)
 163 | 162,On Golden Pond (1981)
 164 | 163,"Return of the Pink Panther, The (1974)"
 165 | 164,"Abyss, The (1989)"
 166 | 165,Jean de Florette (1986)
 167 | 166,Manon of the Spring (Manon des sources) (1986)
 168 | 167,Private Benjamin (1980)
 169 | 168,Monty Python and the Holy Grail (1974)
 170 | 169,"Wrong Trousers, The (1993)"
 171 | 170,Cinema Paradiso (1988)
 172 | 171,Delicatessen (1991)
 173 | 172,"Empire Strikes Back, The (1980)"
 174 | 173,"Princess Bride, The (1987)"
 175 | 174,Raiders of the Lost Ark (1981)
 176 | 175,Brazil (1985)
 177 | 176,Aliens (1986)
 178 | 177,"Good, The Bad and The Ugly, The (1966)"
 179 | 178,12 Angry Men (1957)
 180 | 179,"Clockwork Orange, A (1971)"
 181 | 180,Apocalypse Now (1979)
 182 | 181,Return of the Jedi (1983)
 183 | 182,GoodFellas (1990)
 184 | 183,Alien (1979)
 185 | 184,Army of Darkness (1993)
 186 | 185,Psycho (1960)
 187 | 186,"Blues Brothers, The (1980)"
 188 | 187,"Godfather: Part II, The (1974)"
 189 | 188,Full Metal Jacket (1987)
 190 | 189,"Grand Day Out, A (1992)"
 191 | 190,Henry V (1989)
 192 | 191,Amadeus (1984)
 193 | 192,Raging Bull (1980)
 194 | 193,"Right Stuff, The (1983)"
 195 | 194,"Sting, The (1973)"
 196 | 195,"Terminator, The (1984)"
 197 | 196,Dead Poets Society (1989)
 198 | 197,"Graduate, The (1967)"
 199 | 198,Nikita (La Femme Nikita) (1990)
 200 | 199,"Bridge on the River Kwai, The (1957)"
 201 | 200,"Shining, The (1980)"
 202 | 201,Evil Dead II (1987)
 203 | 202,Groundhog Day (1993)
 204 | 203,Unforgiven (1992)
 205 | 204,Back to the Future (1985)
 206 | 205,Patton (1970)
 207 | 206,Akira (1988)
 208 | 207,Cyrano de Bergerac (1990)
 209 | 208,Young Frankenstein (1974)
 210 | 209,This Is Spinal Tap (1984)
 211 | 210,Indiana Jones and the Last Crusade (1989)
 212 | 211,M*A*S*H (1970)
 213 | 212,"Unbearable Lightness of Being, The (1988)"
 214 | 213,"Room with a View, A (1986)"
 215 | 214,Pink Floyd - The Wall (1982)
 216 | 215,Field of Dreams (1989)
 217 | 216,When Harry Met Sally... (1989)
 218 | 217,Bram Stoker's Dracula (1992)
 219 | 218,Cape Fear (1991)
 220 | 219,"Nightmare on Elm Street, A (1984)"
 221 | 220,"Mirror Has Two Faces, The (1996)"
 222 | 221,Breaking the Waves (1996)
 223 | 222,Star Trek: First Contact (1996)
 224 | 223,Sling Blade (1996)
 225 | 224,Ridicule (1996)
 226 | 225,101 Dalmatians (1996)
 227 | 226,Die Hard 2 (1990)
 228 | 227,Star Trek VI: The Undiscovered Country (1991)
 229 | 228,Star Trek: The Wrath of Khan (1982)
 230 | 229,Star Trek III: The Search for Spock (1984)
 231 | 230,Star Trek IV: The Voyage Home (1986)
 232 | 231,Batman Returns (1992)
 233 | 232,Young Guns (1988)
 234 | 233,Under Siege (1992)
 235 | 234,Jaws (1975)
 236 | 235,Mars Attacks! (1996)
 237 | 236,Citizen Ruth (1996)
 238 | 237,Jerry Maguire (1996)
 239 | 238,Raising Arizona (1987)
 240 | 239,Sneakers (1992)
 241 | 240,Beavis and Butt-head Do America (1996)
 242 | 241,"Last of the Mohicans, The (1992)"
 243 | 242,Kolya (1996)
 244 | 243,Jungle2Jungle (1997)
 245 | 244,Smilla's Sense of Snow (1997)
 246 | 245,"Devil's Own, The (1997)"
 247 | 246,Chasing Amy (1997)
 248 | 247,Turbo: A Power Rangers Movie (1997)
 249 | 248,Grosse Pointe Blank (1997)
 250 | 249,Austin Powers: International Man of Mystery (1997)
 251 | 250,"Fifth Element, The (1997)"
 252 | 251,Shall We Dance? (1996)
 253 | 252,"Lost World: Jurassic Park, The (1997)"
 254 | 253,"Pillow Book, The (1995)"
 255 | 254,Batman & Robin (1997)
 256 | 255,My Best Friend's Wedding (1997)
 257 | 256,When the Cats Away (Chacun cherche son chat) (1996)
 258 | 257,Men in Black (1997)
 259 | 258,Contact (1997)
 260 | 259,George of the Jungle (1997)
 261 | 260,Event Horizon (1997)
 262 | 261,Air Bud (1997)
 263 | 262,In the Company of Men (1997)
 264 | 263,Steel (1997)
 265 | 264,Mimic (1997)
 266 | 265,"Hunt for Red October, The (1990)"
 267 | 266,Kull the Conqueror (1997)
 268 | 267,unknown
 269 | 268,Chasing Amy (1997)
 270 | 269,"Full Monty, The (1997)"
 271 | 270,Gattaca (1997)
 272 | 271,Starship Troopers (1997)
 273 | 272,Good Will Hunting (1997)
 274 | 273,Heat (1995)
 275 | 274,Sabrina (1995)
 276 | 275,Sense and Sensibility (1995)
 277 | 276,Leaving Las Vegas (1995)
 278 | 277,Restoration (1995)
 279 | 278,Bed of Roses (1996)
 280 | 279,Once Upon a Time... When We Were Colored (1995)
 281 | 280,Up Close and Personal (1996)
 282 | 281,"River Wild, The (1994)"
 283 | 282,"Time to Kill, A (1996)"
 284 | 283,Emma (1996)
 285 | 284,Tin Cup (1996)
 286 | 285,Secrets & Lies (1996)
 287 | 286,"English Patient, The (1996)"
 288 | 287,Marvin's Room (1996)
 289 | 288,Scream (1996)
 290 | 289,Evita (1996)
 291 | 290,Fierce Creatures (1997)
 292 | 291,Absolute Power (1997)
 293 | 292,Rosewood (1997)
 294 | 293,Donnie Brasco (1997)
 295 | 294,Liar Liar (1997)
 296 | 295,Breakdown (1997)
 297 | 296,"Promesse, La (1996)"
 298 | 297,Ulee's Gold (1997)
 299 | 298,Face/Off (1997)
 300 | 299,Hoodlum (1997)
 301 | 300,Air Force One (1997)
 302 | 301,In & Out (1997)
 303 | 302,L.A. Confidential (1997)
 304 | 303,Ulee's Gold (1997)
 305 | 304,Fly Away Home (1996)
 306 | 305,"Ice Storm, The (1997)"
 307 | 306,"Mrs. Brown (Her Majesty, Mrs. Brown) (1997)"
 308 | 307,"Devil's Advocate, The (1997)"
 309 | 308,FairyTale: A True Story (1997)
 310 | 309,Deceiver (1997)
 311 | 310,"Rainmaker, The (1997)"
 312 | 311,"Wings of the Dove, The (1997)"
 313 | 312,Midnight in the Garden of Good and Evil (1997)
 314 | 313,Titanic (1997)
 315 | 314,3 Ninjas: High Noon At Mega Mountain (1998)
 316 | 315,Apt Pupil (1998)
 317 | 316,As Good As It Gets (1997)
 318 | 317,In the Name of the Father (1993)
 319 | 318,Schindler's List (1993)
 320 | 319,Everyone Says I Love You (1996)
 321 | 320,Paradise Lost: The Child Murders at Robin Hood Hills (1996)
 322 | 321,Mother (1996)
 323 | 322,Murder at 1600 (1997)
 324 | 323,Dante's Peak (1997)
 325 | 324,Lost Highway (1997)
 326 | 325,Crash (1996)
 327 | 326,G.I. Jane (1997)
 328 | 327,Cop Land (1997)
 329 | 328,Conspiracy Theory (1997)
 330 | 329,Desperate Measures (1998)
 331 | 330,187 (1997)
 332 | 331,"Edge, The (1997)"
 333 | 332,Kiss the Girls (1997)
 334 | 333,"Game, The (1997)"
 335 | 334,U Turn (1997)
 336 | 335,How to Be a Player (1997)
 337 | 336,Playing God (1997)
 338 | 337,"House of Yes, The (1997)"
 339 | 338,Bean (1997)
 340 | 339,Mad City (1997)
 341 | 340,Boogie Nights (1997)
 342 | 341,Critical Care (1997)
 343 | 342,"Man Who Knew Too Little, The (1997)"
 344 | 343,Alien: Resurrection (1997)
 345 | 344,"Apostle, The (1997)"
 346 | 345,Deconstructing Harry (1997)
 347 | 346,Jackie Brown (1997)
 348 | 347,Wag the Dog (1997)
 349 | 348,Desperate Measures (1998)
 350 | 349,Hard Rain (1998)
 351 | 350,Fallen (1998)
 352 | 351,"Prophecy II, The (1998)"
 353 | 352,Spice World (1997)
 354 | 353,Deep Rising (1998)
 355 | 354,"Wedding Singer, The (1998)"
 356 | 355,Sphere (1998)
 357 | 356,"Client, The (1994)"
 358 | 357,One Flew Over the Cuckoo's Nest (1975)
 359 | 358,Spawn (1997)
 360 | 359,"Assignment, The (1997)"
 361 | 360,Wonderland (1997)
 362 | 361,Incognito (1997)
 363 | 362,Blues Brothers 2000 (1998)
 364 | 363,Sudden Death (1995)
 365 | 364,Ace Ventura: When Nature Calls (1995)
 366 | 365,Powder (1995)
 367 | 366,Dangerous Minds (1995)
 368 | 367,Clueless (1995)
 369 | 368,Bio-Dome (1996)
 370 | 369,Black Sheep (1996)
 371 | 370,Mary Reilly (1996)
 372 | 371,"Bridges of Madison County, The (1995)"
 373 | 372,Jeffrey (1995)
 374 | 373,Judge Dredd (1995)
 375 | 374,Mighty Morphin Power Rangers: The Movie (1995)
 376 | 375,Showgirls (1995)
 377 | 376,Houseguest (1994)
 378 | 377,Heavyweights (1994)
 379 | 378,Miracle on 34th Street (1994)
 380 | 379,Tales From the Crypt Presents: Demon Knight (1995)
 381 | 380,Star Trek: Generations (1994)
 382 | 381,Muriel's Wedding (1994)
 383 | 382,"Adventures of Priscilla, Queen of the Desert, The (1994)"
 384 | 383,"Flintstones, The (1994)"
 385 | 384,Naked Gun 33 1/3: The Final Insult (1994)
 386 | 385,True Lies (1994)
 387 | 386,Addams Family Values (1993)
 388 | 387,"Age of Innocence, The (1993)"
 389 | 388,Beverly Hills Cop III (1994)
 390 | 389,Black Beauty (1994)
 391 | 390,Fear of a Black Hat (1993)
 392 | 391,Last Action Hero (1993)
 393 | 392,"Man Without a Face, The (1993)"
 394 | 393,Mrs. Doubtfire (1993)
 395 | 394,Radioland Murders (1994)
 396 | 395,Robin Hood: Men in Tights (1993)
 397 | 396,Serial Mom (1994)
 398 | 397,Striking Distance (1993)
 399 | 398,Super Mario Bros. (1993)
 400 | 399,"Three Musketeers, The (1993)"
 401 | 400,"Little Rascals, The (1994)"
 402 | 401,"Brady Bunch Movie, The (1995)"
 403 | 402,Ghost (1990)
 404 | 403,Batman (1989)
 405 | 404,Pinocchio (1940)
 406 | 405,Mission: Impossible (1996)
 407 | 406,Thinner (1996)
 408 | 407,Spy Hard (1996)
 409 | 408,"Close Shave, A (1995)"
 410 | 409,Jack (1996)
 411 | 410,Kingpin (1996)
 412 | 411,"Nutty Professor, The (1996)"
 413 | 412,"Very Brady Sequel, A (1996)"
 414 | 413,Tales from the Crypt Presents: Bordello of Blood (1996)
 415 | 414,My Favorite Year (1982)
 416 | 415,"Apple Dumpling Gang, The (1975)"
 417 | 416,Old Yeller (1957)
 418 | 417,"Parent Trap, The (1961)"
 419 | 418,Cinderella (1950)
 420 | 419,Mary Poppins (1964)
 421 | 420,Alice in Wonderland (1951)
 422 | 421,William Shakespeare's Romeo and Juliet (1996)
 423 | 422,Aladdin and the King of Thieves (1996)
 424 | 423,E.T. the Extra-Terrestrial (1982)
 425 | 424,Children of the Corn: The Gathering (1996)
 426 | 425,Bob Roberts (1992)
 427 | 426,"Transformers: The Movie, The (1986)"
 428 | 427,To Kill a Mockingbird (1962)
 429 | 428,Harold and Maude (1971)
 430 | 429,"Day the Earth Stood Still, The (1951)"
 431 | 430,Duck Soup (1933)
 432 | 431,Highlander (1986)
 433 | 432,Fantasia (1940)
 434 | 433,Heathers (1989)
 435 | 434,Forbidden Planet (1956)
 436 | 435,Butch Cassidy and the Sundance Kid (1969)
 437 | 436,"American Werewolf in London, An (1981)"
 438 | 437,Amityville 1992: It's About Time (1992)
 439 | 438,Amityville 3-D (1983)
 440 | 439,Amityville: A New Generation (1993)
 441 | 440,Amityville II: The Possession (1982)
 442 | 441,"Amityville Horror, The (1979)"
 443 | 442,"Amityville Curse, The (1990)"
 444 | 443,"Birds, The (1963)"
 445 | 444,"Blob, The (1958)"
 446 | 445,"Body Snatcher, The (1945)"
 447 | 446,Burnt Offerings (1976)
 448 | 447,Carrie (1976)
 449 | 448,"Omen, The (1976)"
 450 | 449,Star Trek: The Motion Picture (1979)
 451 | 450,Star Trek V: The Final Frontier (1989)
 452 | 451,Grease (1978)
 453 | 452,Jaws 2 (1978)
 454 | 453,Jaws 3-D (1983)
 455 | 454,Bastard Out of Carolina (1996)
 456 | 455,Jackie Chan's First Strike (1996)
 457 | 456,Beverly Hills Ninja (1997)
 458 | 457,Free Willy 3: The Rescue (1997)
 459 | 458,Nixon (1995)
 460 | 459,"Cry, the Beloved Country (1995)"
 461 | 460,"Crossing Guard, The (1995)"
 462 | 461,Smoke (1995)
 463 | 462,Like Water For Chocolate (Como agua para chocolate) (1992)
 464 | 463,"Secret of Roan Inish, The (1994)"
 465 | 464,Vanya on 42nd Street (1994)
 466 | 465,"Jungle Book, The (1994)"
 467 | 466,Red Rock West (1992)
 468 | 467,"Bronx Tale, A (1993)"
 469 | 468,Rudy (1993)
 470 | 469,Short Cuts (1993)
 471 | 470,Tombstone (1993)
 472 | 471,Courage Under Fire (1996)
 473 | 472,Dragonheart (1996)
 474 | 473,James and the Giant Peach (1996)
 475 | 474,Dr. Strangelove or: How I Learned to Stop Worrying and Love the Bomb (1963)
 476 | 475,Trainspotting (1996)
 477 | 476,"First Wives Club, The (1996)"
 478 | 477,Matilda (1996)
 479 | 478,"Philadelphia Story, The (1940)"
 480 | 479,Vertigo (1958)
 481 | 480,North by Northwest (1959)
 482 | 481,"Apartment, The (1960)"
 483 | 482,Some Like It Hot (1959)
 484 | 483,Casablanca (1942)
 485 | 484,"Maltese Falcon, The (1941)"
 486 | 485,My Fair Lady (1964)
 487 | 486,Sabrina (1954)
 488 | 487,Roman Holiday (1953)
 489 | 488,Sunset Blvd. (1950)
 490 | 489,Notorious (1946)
 491 | 490,To Catch a Thief (1955)
 492 | 491,"Adventures of Robin Hood, The (1938)"
 493 | 492,East of Eden (1955)
 494 | 493,"Thin Man, The (1934)"
 495 | 494,His Girl Friday (1940)
 496 | 495,Around the World in 80 Days (1956)
 497 | 496,It's a Wonderful Life (1946)
 498 | 497,Bringing Up Baby (1938)
 499 | 498,"African Queen, The (1951)"
 500 | 499,Cat on a Hot Tin Roof (1958)
 501 | 500,Fly Away Home (1996)
 502 | 501,Dumbo (1941)
 503 | 502,Bananas (1971)
 504 | 503,"Candidate, The (1972)"
 505 | 504,Bonnie and Clyde (1967)
 506 | 505,Dial M for Murder (1954)
 507 | 506,Rebel Without a Cause (1955)
 508 | 507,"Streetcar Named Desire, A (1951)"
 509 | 508,"People vs. Larry Flynt, The (1996)"
 510 | 509,My Left Foot (1989)
 511 | 510,"Magnificent Seven, The (1954)"
 512 | 511,Lawrence of Arabia (1962)
 513 | 512,Wings of Desire (1987)
 514 | 513,"Third Man, The (1949)"
 515 | 514,Annie Hall (1977)
 516 | 515,"Boot, Das (1981)"
 517 | 516,Local Hero (1983)
 518 | 517,Manhattan (1979)
 519 | 518,Miller's Crossing (1990)
 520 | 519,"Treasure of the Sierra Madre, The (1948)"
 521 | 520,"Great Escape, The (1963)"
 522 | 521,"Deer Hunter, The (1978)"
 523 | 522,Down by Law (1986)
 524 | 523,Cool Hand Luke (1967)
 525 | 524,"Great Dictator, The (1940)"
 526 | 525,"Big Sleep, The (1946)"
 527 | 526,Ben-Hur (1959)
 528 | 527,Gandhi (1982)
 529 | 528,"Killing Fields, The (1984)"
 530 | 529,My Life as a Dog (Mitt liv som hund) (1985)
 531 | 530,"Man Who Would Be King, The (1975)"
 532 | 531,Shine (1996)
 533 | 532,Kama Sutra: A Tale of Love (1996)
 534 | 533,"Daytrippers, The (1996)"
 535 | 534,Traveller (1997)
 536 | 535,Addicted to Love (1997)
 537 | 536,Ponette (1996)
 538 | 537,My Own Private Idaho (1991)
 539 | 538,Anastasia (1997)
 540 | 539,Mouse Hunt (1997)
 541 | 540,Money Train (1995)
 542 | 541,Mortal Kombat (1995)
 543 | 542,Pocahontas (1995)
 544 | 543,"Misérables, Les (1995)"
 545 | 544,Things to Do in Denver when You're Dead (1995)
 546 | 545,Vampire in Brooklyn (1995)
 547 | 546,Broken Arrow (1996)
 548 | 547,"Young Poisoner's Handbook, The (1995)"
 549 | 548,"NeverEnding Story III, The (1994)"
 550 | 549,Rob Roy (1995)
 551 | 550,Die Hard: With a Vengeance (1995)
 552 | 551,Lord of Illusions (1995)
 553 | 552,Species (1995)
 554 | 553,"Walk in the Clouds, A (1995)"
 555 | 554,Waterworld (1995)
 556 | 555,White Man's Burden (1995)
 557 | 556,Wild Bill (1995)
 558 | 557,Farinelli: il castrato (1994)
 559 | 558,Heavenly Creatures (1994)
 560 | 559,Interview with the Vampire (1994)
 561 | 560,"Kid in King Arthur's Court, A (1995)"
 562 | 561,Mary Shelley's Frankenstein (1994)
 563 | 562,"Quick and the Dead, The (1995)"
 564 | 563,Stephen King's The Langoliers (1995)
 565 | 564,Tales from the Hood (1995)
 566 | 565,Village of the Damned (1995)
 567 | 566,Clear and Present Danger (1994)
 568 | 567,Wes Craven's New Nightmare (1994)
 569 | 568,Speed (1994)
 570 | 569,Wolf (1994)
 571 | 570,Wyatt Earp (1994)
 572 | 571,Another Stakeout (1993)
 573 | 572,Blown Away (1994)
 574 | 573,Body Snatchers (1993)
 575 | 574,Boxing Helena (1993)
 576 | 575,City Slickers II: The Legend of Curly's Gold (1994)
 577 | 576,Cliffhanger (1993)
 578 | 577,Coneheads (1993)
 579 | 578,Demolition Man (1993)
 580 | 579,Fatal Instinct (1993)
 581 | 580,"Englishman Who Went Up a Hill, But Came Down a Mountain, The (1995)"
 582 | 581,Kalifornia (1993)
 583 | 582,"Piano, The (1993)"
 584 | 583,Romeo Is Bleeding (1993)
 585 | 584,"Secret Garden, The (1993)"
 586 | 585,Son in Law (1993)
 587 | 586,Terminal Velocity (1994)
 588 | 587,"Hour of the Pig, The (1993)"
 589 | 588,Beauty and the Beast (1991)
 590 | 589,"Wild Bunch, The (1969)"
 591 | 590,Hellraiser: Bloodline (1996)
 592 | 591,Primal Fear (1996)
 593 | 592,True Crime (1995)
 594 | 593,Stalingrad (1993)
 595 | 594,Heavy (1995)
 596 | 595,"Fan, The (1996)"
 597 | 596,"Hunchback of Notre Dame, The (1996)"
 598 | 597,Eraser (1996)
 599 | 598,"Big Squeeze, The (1996)"
 600 | 599,Police Story 4: Project S (Chao ji ji hua) (1993)
 601 | 600,Daniel Defoe's Robinson Crusoe (1996)
 602 | 601,For Whom the Bell Tolls (1943)
 603 | 602,"American in Paris, An (1951)"
 604 | 603,Rear Window (1954)
 605 | 604,It Happened One Night (1934)
 606 | 605,Meet Me in St. Louis (1944)
 607 | 606,All About Eve (1950)
 608 | 607,Rebecca (1940)
 609 | 608,Spellbound (1945)
 610 | 609,Father of the Bride (1950)
 611 | 610,Gigi (1958)
 612 | 611,Laura (1944)
 613 | 612,Lost Horizon (1937)
 614 | 613,My Man Godfrey (1936)
 615 | 614,Giant (1956)
 616 | 615,"39 Steps, The (1935)"
 617 | 616,Night of the Living Dead (1968)
 618 | 617,"Blue Angel, The (Blaue Engel, Der) (1930)"
 619 | 618,Picnic (1955)
 620 | 619,Extreme Measures (1996)
 621 | 620,"Chamber, The (1996)"
 622 | 621,"Davy Crockett, King of the Wild Frontier (1955)"
 623 | 622,Swiss Family Robinson (1960)
 624 | 623,Angels in the Outfield (1994)
 625 | 624,"Three Caballeros, The (1945)"
 626 | 625,"Sword in the Stone, The (1963)"
 627 | 626,So Dear to My Heart (1949)
 628 | 627,Robin Hood: Prince of Thieves (1991)
 629 | 628,Sleepers (1996)
 630 | 629,Victor/Victoria (1982)
 631 | 630,"Great Race, The (1965)"
 632 | 631,"Crying Game, The (1992)"
 633 | 632,Sophie's Choice (1982)
 634 | 633,"Christmas Carol, A (1938)"
 635 | 634,Microcosmos: Le peuple de l'herbe (1996)
 636 | 635,"Fog, The (1980)"
 637 | 636,Escape from New York (1981)
 638 | 637,"Howling, The (1981)"
 639 | 638,"Return of Martin Guerre, The (Retour de Martin Guerre, Le) (1982)"
 640 | 639,"Tin Drum, The (Blechtrommel, Die) (1979)"
 641 | 640,"Cook the Thief His Wife & Her Lover, The (1989)"
 642 | 641,Paths of Glory (1957)
 643 | 642,"Grifters, The (1990)"
 644 | 643,The Innocent (1994)
 645 | 644,"Thin Blue Line, The (1988)"
 646 | 645,Paris Is Burning (1990)
 647 | 646,Once Upon a Time in the West (1969)
 648 | 647,Ran (1985)
 649 | 648,"Quiet Man, The (1952)"
 650 | 649,Once Upon a Time in America (1984)
 651 | 650,"Seventh Seal, The (Sjunde inseglet, Det) (1957)"
 652 | 651,Glory (1989)
 653 | 652,Rosencrantz and Guildenstern Are Dead (1990)
 654 | 653,Touch of Evil (1958)
 655 | 654,Chinatown (1974)
 656 | 655,Stand by Me (1986)
 657 | 656,M (1931)
 658 | 657,"Manchurian Candidate, The (1962)"
 659 | 658,Pump Up the Volume (1990)
 660 | 659,Arsenic and Old Lace (1944)
 661 | 660,Fried Green Tomatoes (1991)
 662 | 661,High Noon (1952)
 663 | 662,Somewhere in Time (1980)
 664 | 663,Being There (1979)
 665 | 664,"Paris, Texas (1984)"
 666 | 665,Alien 3 (1992)
 667 | 666,Blood For Dracula (Andy Warhol's Dracula) (1974)
 668 | 667,Audrey Rose (1977)
 669 | 668,Blood Beach (1981)
 670 | 669,Body Parts (1991)
 671 | 670,Body Snatchers (1993)
 672 | 671,Bride of Frankenstein (1935)
 673 | 672,Candyman (1992)
 674 | 673,Cape Fear (1962)
 675 | 674,Cat People (1982)
 676 | 675,"Nosferatu (Nosferatu, eine Symphonie des Grauens) (1922)"
 677 | 676,"Crucible, The (1996)"
 678 | 677,Fire on the Mountain (1996)
 679 | 678,Volcano (1997)
 680 | 679,Conan the Barbarian (1981)
 681 | 680,Kull the Conqueror (1997)
 682 | 681,Wishmaster (1997)
 683 | 682,I Know What You Did Last Summer (1997)
 684 | 683,Rocket Man (1997)
 685 | 684,In the Line of Fire (1993)
 686 | 685,Executive Decision (1996)
 687 | 686,"Perfect World, A (1993)"
 688 | 687,McHale's Navy (1997)
 689 | 688,Leave It to Beaver (1997)
 690 | 689,"Jackal, The (1997)"
 691 | 690,Seven Years in Tibet (1997)
 692 | 691,Dark City (1998)
 693 | 692,"American President, The (1995)"
 694 | 693,Casino (1995)
 695 | 694,Persuasion (1995)
 696 | 695,Kicking and Screaming (1995)
 697 | 696,City Hall (1996)
 698 | 697,"Basketball Diaries, The (1995)"
 699 | 698,"Browning Version, The (1994)"
 700 | 699,Little Women (1994)
 701 | 700,Miami Rhapsody (1995)
 702 | 701,"Wonderful, Horrible Life of Leni Riefenstahl, The (1993)"
 703 | 702,Barcelona (1994)
 704 | 703,Widows' Peak (1994)
 705 | 704,"House of the Spirits, The (1993)"
 706 | 705,Singin' in the Rain (1952)
 707 | 706,Bad Moon (1996)
 708 | 707,Enchanted April (1991)
 709 | 708,"Sex, Lies, and Videotape (1989)"
 710 | 709,Strictly Ballroom (1992)
 711 | 710,Better Off Dead... (1985)
 712 | 711,"Substance of Fire, The (1996)"
 713 | 712,Tin Men (1987)
 714 | 713,Othello (1995)
 715 | 714,Carrington (1995)
 716 | 715,To Die For (1995)
 717 | 716,Home for the Holidays (1995)
 718 | 717,"Juror, The (1996)"
 719 | 718,In the Bleak Midwinter (1995)
 720 | 719,Canadian Bacon (1994)
 721 | 720,First Knight (1995)
 722 | 721,Mallrats (1995)
 723 | 722,Nine Months (1995)
 724 | 723,Boys on the Side (1995)
 725 | 724,Circle of Friends (1995)
 726 | 725,Exit to Eden (1994)
 727 | 726,Fluke (1995)
 728 | 727,Immortal Beloved (1994)
 729 | 728,Junior (1994)
 730 | 729,Nell (1994)
 731 | 730,"Queen Margot (Reine Margot, La) (1994)"
 732 | 731,"Corrina, Corrina (1994)"
 733 | 732,Dave (1993)
 734 | 733,Go Fish (1994)
 735 | 734,Made in America (1993)
 736 | 735,Philadelphia (1993)
 737 | 736,Shadowlands (1993)
 738 | 737,Sirens (1994)
 739 | 738,Threesome (1994)
 740 | 739,Pretty Woman (1990)
 741 | 740,Jane Eyre (1996)
 742 | 741,"Last Supper, The (1995)"
 743 | 742,Ransom (1996)
 744 | 743,"Crow: City of Angels, The (1996)"
 745 | 744,Michael Collins (1996)
 746 | 745,"Ruling Class, The (1972)"
 747 | 746,Real Genius (1985)
 748 | 747,Benny & Joon (1993)
 749 | 748,"Saint, The (1997)"
 750 | 749,"MatchMaker, The (1997)"
 751 | 750,Amistad (1997)
 752 | 751,Tomorrow Never Dies (1997)
 753 | 752,"Replacement Killers, The (1998)"
 754 | 753,Burnt By the Sun (1994)
 755 | 754,Red Corner (1997)
 756 | 755,Jumanji (1995)
 757 | 756,Father of the Bride Part II (1995)
 758 | 757,Across the Sea of Time (1995)
 759 | 758,Lawnmower Man 2: Beyond Cyberspace (1996)
 760 | 759,Fair Game (1995)
 761 | 760,Screamers (1995)
 762 | 761,Nick of Time (1995)
 763 | 762,Beautiful Girls (1996)
 764 | 763,Happy Gilmore (1996)
 765 | 764,If Lucy Fell (1996)
 766 | 765,Boomerang (1992)
 767 | 766,Man of the Year (1995)
 768 | 767,"Addiction, The (1995)"
 769 | 768,Casper (1995)
 770 | 769,Congo (1995)
 771 | 770,Devil in a Blue Dress (1995)
 772 | 771,Johnny Mnemonic (1995)
 773 | 772,Kids (1995)
 774 | 773,Mute Witness (1994)
 775 | 774,"Prophecy, The (1995)"
 776 | 775,Something to Talk About (1995)
 777 | 776,Three Wishes (1995)
 778 | 777,Castle Freak (1995)
 779 | 778,Don Juan DeMarco (1995)
 780 | 779,Drop Zone (1994)
 781 | 780,Dumb & Dumber (1994)
 782 | 781,French Kiss (1995)
 783 | 782,Little Odessa (1994)
 784 | 783,Milk Money (1994)
 785 | 784,Beyond Bedlam (1993)
 786 | 785,Only You (1994)
 787 | 786,"Perez Family, The (1995)"
 788 | 787,Roommates (1995)
 789 | 788,Relative Fear (1994)
 790 | 789,Swimming with Sharks (1995)
 791 | 790,Tommy Boy (1995)
 792 | 791,"Baby-Sitters Club, The (1995)"
 793 | 792,Bullets Over Broadway (1994)
 794 | 793,Crooklyn (1994)
 795 | 794,It Could Happen to You (1994)
 796 | 795,Richie Rich (1994)
 797 | 796,Speechless (1994)
 798 | 797,Timecop (1994)
 799 | 798,Bad Company (1995)
 800 | 799,Boys Life (1995)
 801 | 800,In the Mouth of Madness (1995)
 802 | 801,"Air Up There, The (1994)"
 803 | 802,Hard Target (1993)
 804 | 803,Heaven & Earth (1993)
 805 | 804,Jimmy Hollywood (1994)
 806 | 805,Manhattan Murder Mystery (1993)
 807 | 806,Menace II Society (1993)
 808 | 807,Poetic Justice (1993)
 809 | 808,"Program, The (1993)"
 810 | 809,Rising Sun (1993)
 811 | 810,"Shadow, The (1994)"
 812 | 811,Thirty-Two Short Films About Glenn Gould (1993)
 813 | 812,Andre (1994)
 814 | 813,"Celluloid Closet, The (1995)"
 815 | 814,"Great Day in Harlem, A (1994)"
 816 | 815,One Fine Day (1996)
 817 | 816,Candyman: Farewell to the Flesh (1995)
 818 | 817,Frisk (1995)
 819 | 818,Girl 6 (1996)
 820 | 819,Eddie (1996)
 821 | 820,Space Jam (1996)
 822 | 821,Mrs. Winterbourne (1996)
 823 | 822,Faces (1968)
 824 | 823,Mulholland Falls (1996)
 825 | 824,"Great White Hype, The (1996)"
 826 | 825,"Arrival, The (1996)"
 827 | 826,"Phantom, The (1996)"
 828 | 827,Daylight (1996)
 829 | 828,Alaska (1996)
 830 | 829,Fled (1996)
 831 | 830,Power 98 (1995)
 832 | 831,Escape from L.A. (1996)
 833 | 832,Bogus (1996)
 834 | 833,Bulletproof (1996)
 835 | 834,Halloween: The Curse of Michael Myers (1995)
 836 | 835,"Gay Divorcee, The (1934)"
 837 | 836,Ninotchka (1939)
 838 | 837,Meet John Doe (1941)
 839 | 838,In the Line of Duty 2 (1987)
 840 | 839,Loch Ness (1995)
 841 | 840,Last Man Standing (1996)
 842 | 841,"Glimmer Man, The (1996)"
 843 | 842,Pollyanna (1960)
 844 | 843,"Shaggy Dog, The (1959)"
 845 | 844,Freeway (1996)
 846 | 845,That Thing You Do! (1996)
 847 | 846,To Gillian on Her 37th Birthday (1996)
 848 | 847,Looking for Richard (1996)
 849 | 848,"Murder, My Sweet (1944)"
 850 | 849,Days of Thunder (1990)
 851 | 850,"Perfect Candidate, A (1996)"
 852 | 851,Two or Three Things I Know About Her (1966)
 853 | 852,"Bloody Child, The (1996)"
 854 | 853,Braindead (1992)
 855 | 854,Bad Taste (1987)
 856 | 855,Diva (1981)
 857 | 856,Night on Earth (1991)
 858 | 857,Paris Was a Woman (1995)
 859 | 858,Amityville: Dollhouse (1996)
 860 | 859,April Fool's Day (1986)
 861 | 860,"Believers, The (1987)"
 862 | 861,Nosferatu a Venezia (1986)
 863 | 862,Jingle All the Way (1996)
 864 | 863,"Garden of Finzi-Contini, The (Giardino dei Finzi-Contini, Il) (1970)"
 865 | 864,My Fellow Americans (1996)
 866 | 865,"Ice Storm, The (1997)"
 867 | 866,Michael (1996)
 868 | 867,"Whole Wide World, The (1996)"
 869 | 868,Hearts and Minds (1996)
 870 | 869,Fools Rush In (1997)
 871 | 870,Touch (1997)
 872 | 871,Vegas Vacation (1997)
 873 | 872,Love Jones (1997)
 874 | 873,Picture Perfect (1997)
 875 | 874,Career Girls (1997)
 876 | 875,She's So Lovely (1997)
 877 | 876,Money Talks (1997)
 878 | 877,Excess Baggage (1997)
 879 | 878,That Darn Cat! (1997)
 880 | 879,"Peacemaker, The (1997)"
 881 | 880,Soul Food (1997)
 882 | 881,Money Talks (1997)
 883 | 882,Washington Square (1997)
 884 | 883,Telling Lies in America (1997)
 885 | 884,Year of the Horse (1997)
 886 | 885,Phantoms (1998)
 887 | 886,"Life Less Ordinary, A (1997)"
 888 | 887,Eve's Bayou (1997)
 889 | 888,One Night Stand (1997)
 890 | 889,"Tango Lesson, The (1997)"
 891 | 890,Mortal Kombat: Annihilation (1997)
 892 | 891,Bent (1997)
 893 | 892,Flubber (1997)
 894 | 893,For Richer or Poorer (1997)
 895 | 894,Home Alone 3 (1997)
 896 | 895,Scream 2 (1997)
 897 | 896,"Sweet Hereafter, The (1997)"
 898 | 897,Time Tracers (1995)
 899 | 898,"Postman, The (1997)"
 900 | 899,"Winter Guest, The (1997)"
 901 | 900,Kundun (1997)
 902 | 901,Mr. Magoo (1997)
 903 | 902,"Big Lebowski, The (1998)"
 904 | 903,Afterglow (1997)
 905 | 904,Ma vie en rose (My Life in Pink) (1997)
 906 | 905,Great Expectations (1998)
 907 | 906,Oscar & Lucinda (1997)
 908 | 907,Vermin (1998)
 909 | 908,Half Baked (1998)
 910 | 909,Dangerous Beauty (1998)
 911 | 910,Nil By Mouth (1997)
 912 | 911,Twilight (1998)
 913 | 912,U.S. Marshalls (1998)
 914 | 913,Love and Death on Long Island (1997)
 915 | 914,Wild Things (1998)
 916 | 915,Primary Colors (1998)
 917 | 916,Lost in Space (1998)
 918 | 917,Mercury Rising (1998)
 919 | 918,City of Angels (1998)
 920 | 919,"City of Lost Children, The (1995)"
 921 | 920,Two Bits (1995)
 922 | 921,Farewell My Concubine (1993)
 923 | 922,Dead Man (1995)
 924 | 923,Raise the Red Lantern (1991)
 925 | 924,White Squall (1996)
 926 | 925,Unforgettable (1996)
 927 | 926,Down Periscope (1996)
 928 | 927,"Flower of My Secret, The (Flor de mi secreto, La) (1995)"
 929 | 928,"Craft, The (1996)"
 930 | 929,Harriet the Spy (1996)
 931 | 930,Chain Reaction (1996)
 932 | 931,"Island of Dr. Moreau, The (1996)"
 933 | 932,First Kid (1996)
 934 | 933,"Funeral, The (1996)"
 935 | 934,"Preacher's Wife, The (1996)"
 936 | 935,Paradise Road (1997)
 937 | 936,Brassed Off (1996)
 938 | 937,"Thousand Acres, A (1997)"
 939 | 938,"Smile Like Yours, A (1997)"
 940 | 939,Murder in the First (1995)
 941 | 940,Airheads (1994)
 942 | 941,With Honors (1994)
 943 | 942,What's Love Got to Do with It (1993)
 944 | 943,Killing Zoe (1994)
 945 | 944,Renaissance Man (1994)
 946 | 945,Charade (1963)
 947 | 946,"Fox and the Hound, The (1981)"
 948 | 947,"Big Blue, The (Grand bleu, Le) (1988)"
 949 | 948,Booty Call (1997)
 950 | 949,How to Make an American Quilt (1995)
 951 | 950,Georgia (1995)
 952 | 951,"Indian in the Cupboard, The (1995)"
 953 | 952,Blue in the Face (1995)
 954 | 953,Unstrung Heroes (1995)
 955 | 954,Unzipped (1995)
 956 | 955,Before Sunrise (1995)
 957 | 956,Nobody's Fool (1994)
 958 | 957,Pushing Hands (1992)
 959 | 958,To Live (Huozhe) (1994)
 960 | 959,Dazed and Confused (1993)
 961 | 960,Naked (1993)
 962 | 961,Orlando (1993)
 963 | 962,Ruby in Paradise (1993)
 964 | 963,Some Folks Call It a Sling Blade (1993)
 965 | 964,"Month by the Lake, A (1995)"
 966 | 965,Funny Face (1957)
 967 | 966,"Affair to Remember, An (1957)"
 968 | 967,Little Lord Fauntleroy (1936)
 969 | 968,"Inspector General, The (1949)"
 970 | 969,Winnie the Pooh and the Blustery Day (1968)
 971 | 970,Hear My Song (1991)
 972 | 971,Mediterraneo (1991)
 973 | 972,Passion Fish (1992)
 974 | 973,Grateful Dead (1995)
 975 | 974,Eye for an Eye (1996)
 976 | 975,Fear (1996)
 977 | 976,Solo (1996)
 978 | 977,"Substitute, The (1996)"
 979 | 978,Heaven's Prisoners (1996)
 980 | 979,"Trigger Effect, The (1996)"
 981 | 980,Mother Night (1996)
 982 | 981,Dangerous Ground (1997)
 983 | 982,Maximum Risk (1996)
 984 | 983,"Rich Man's Wife, The (1996)"
 985 | 984,Shadow Conspiracy (1997)
 986 | 985,Blood & Wine (1997)
 987 | 986,Turbulence (1997)
 988 | 987,Underworld (1997)
 989 | 988,"Beautician and the Beast, The (1997)"
 990 | 989,Cats Don't Dance (1997)
 991 | 990,Anna Karenina (1997)
 992 | 991,Keys to Tulsa (1997)
 993 | 992,Head Above Water (1996)
 994 | 993,Hercules (1997)
 995 | 994,"Last Time I Committed Suicide, The (1997)"
 996 | 995,"Kiss Me, Guido (1997)"
 997 | 996,"Big Green, The (1995)"
 998 | 997,Stuart Saves His Family (1995)
 999 | 998,Cabin Boy (1994)
1000 | 999,Clean Slate (1994)
1001 | 1000,Lightning Jack (1994)
1002 | 1001,"Stupids, The (1996)"
1003 | 1002,"Pest, The (1997)"
1004 | 1003,That Darn Cat! (1997)
1005 | 1004,Geronimo: An American Legend (1993)
1006 | 1005,"Double vie de Véronique, La (Double Life of Veronique, The) (1991)"
1007 | 1006,Until the End of the World (Bis ans Ende der Welt) (1991)
1008 | 1007,Waiting for Guffman (1996)
1009 | 1008,I Shot Andy Warhol (1996)
1010 | 1009,Stealing Beauty (1996)
1011 | 1010,Basquiat (1996)
1012 | 1011,2 Days in the Valley (1996)
1013 | 1012,Private Parts (1997)
1014 | 1013,Anaconda (1997)
1015 | 1014,Romy and Michele's High School Reunion (1997)
1016 | 1015,Shiloh (1997)
1017 | 1016,Con Air (1997)
1018 | 1017,Trees Lounge (1996)
1019 | 1018,Tie Me Up! Tie Me Down! (1990)
1020 | 1019,"Die xue shuang xiong (Killer, The) (1989)"
1021 | 1020,Gaslight (1944)
1022 | 1021,8 1/2 (1963)
1023 | 1022,"Fast, Cheap & Out of Control (1997)"
1024 | 1023,Fathers' Day (1997)
1025 | 1024,Mrs. Dalloway (1997)
1026 | 1025,Fire Down Below (1997)
1027 | 1026,"Lay of the Land, The (1997)"
1028 | 1027,"Shooter, The (1995)"
1029 | 1028,Grumpier Old Men (1995)
1030 | 1029,Jury Duty (1995)
1031 | 1030,"Beverly Hillbillies, The (1993)"
1032 | 1031,Lassie (1994)
1033 | 1032,Little Big League (1994)
1034 | 1033,Homeward Bound II: Lost in San Francisco (1996)
1035 | 1034,"Quest, The (1996)"
1036 | 1035,Cool Runnings (1993)
1037 | 1036,Drop Dead Fred (1991)
1038 | 1037,Grease 2 (1982)
1039 | 1038,Switchback (1997)
1040 | 1039,Hamlet (1996)
1041 | 1040,Two if by Sea (1996)
1042 | 1041,Forget Paris (1995)
1043 | 1042,Just Cause (1995)
1044 | 1043,Rent-a-Kid (1995)
1045 | 1044,"Paper, The (1994)"
1046 | 1045,Fearless (1993)
1047 | 1046,Malice (1993)
1048 | 1047,Multiplicity (1996)
1049 | 1048,She's the One (1996)
1050 | 1049,House Arrest (1996)
1051 | 1050,"Ghost and Mrs. Muir, The (1947)"
1052 | 1051,"Associate, The (1996)"
1053 | 1052,Dracula: Dead and Loving It (1995)
1054 | 1053,Now and Then (1995)
1055 | 1054,Mr. Wrong (1996)
1056 | 1055,"Simple Twist of Fate, A (1994)"
1057 | 1056,Cronos (1992)
1058 | 1057,"Pallbearer, The (1996)"
1059 | 1058,"War, The (1994)"
1060 | 1059,Don't Be a Menace to South Central While Drinking Your Juice in the Hood (1996)
1061 | 1060,"Adventures of Pinocchio, The (1996)"
1062 | 1061,"Evening Star, The (1996)"
1063 | 1062,Four Days in September (1997)
1064 | 1063,"Little Princess, A (1995)"
1065 | 1064,Crossfire (1947)
1066 | 1065,Koyaanisqatsi (1983)
1067 | 1066,Balto (1995)
1068 | 1067,Bottle Rocket (1996)
1069 | 1068,"Star Maker, The (Uomo delle stelle, L') (1995)"
1070 | 1069,Amateur (1994)
1071 | 1070,Living in Oblivion (1995)
1072 | 1071,Party Girl (1995)
1073 | 1072,"Pyromaniac's Love Story, A (1995)"
1074 | 1073,Shallow Grave (1994)
1075 | 1074,Reality Bites (1994)
1076 | 1075,"Man of No Importance, A (1994)"
1077 | 1076,"Pagemaster, The (1994)"
1078 | 1077,Love and a .45 (1994)
1079 | 1078,Oliver & Company (1988)
1080 | 1079,Joe's Apartment (1996)
1081 | 1080,Celestial Clockwork (1994)
1082 | 1081,Curdled (1996)
1083 | 1082,Female Perversions (1996)
1084 | 1083,Albino Alligator (1996)
1085 | 1084,Anne Frank Remembered (1995)
1086 | 1085,Carried Away (1996)
1087 | 1086,It's My Party (1995)
1088 | 1087,Bloodsport 2 (1995)
1089 | 1088,Double Team (1997)
1090 | 1089,Speed 2: Cruise Control (1997)
1091 | 1090,Sliver (1993)
1092 | 1091,Pete's Dragon (1977)
1093 | 1092,Dear God (1996)
1094 | 1093,Live Nude Girls (1995)
1095 | 1094,"Thin Line Between Love and Hate, A (1996)"
1096 | 1095,High School High (1996)
1097 | 1096,Commandments (1997)
1098 | 1097,"Hate (Haine, La) (1995)"
1099 | 1098,Flirting With Disaster (1996)
1100 | 1099,"Red Firecracker, Green Firecracker (1994)"
1101 | 1100,What Happened Was... (1994)
1102 | 1101,Six Degrees of Separation (1993)
1103 | 1102,Two Much (1996)
1104 | 1103,Trust (1990)
1105 | 1104,C'est arrivé près de chez vous (1992)
1106 | 1105,Firestorm (1998)
1107 | 1106,"Newton Boys, The (1998)"
1108 | 1107,Beyond Rangoon (1995)
1109 | 1108,Feast of July (1995)
1110 | 1109,Death and the Maiden (1994)
1111 | 1110,Tank Girl (1995)
1112 | 1111,Double Happiness (1994)
1113 | 1112,Cobb (1994)
1114 | 1113,Mrs. Parker and the Vicious Circle (1994)
1115 | 1114,Faithful (1996)
1116 | 1115,Twelfth Night (1996)
1117 | 1116,"Mark of Zorro, The (1940)"
1118 | 1117,Surviving Picasso (1996)
1119 | 1118,Up in Smoke (1978)
1120 | 1119,Some Kind of Wonderful (1987)
1121 | 1120,I'm Not Rappaport (1996)
1122 | 1121,"Umbrellas of Cherbourg, The (Parapluies de Cherbourg, Les) (1964)"
1123 | 1122,They Made Me a Criminal (1939)
1124 | 1123,"Last Time I Saw Paris, The (1954)"
1125 | 1124,"Farewell to Arms, A (1932)"
1126 | 1125,"Innocents, The (1961)"
1127 | 1126,"Old Man and the Sea, The (1958)"
1128 | 1127,"Truman Show, The (1998)"
1129 | 1128,Heidi Fleiss: Hollywood Madam (1995) 
1130 | 1129,Chungking Express (1994)
1131 | 1130,Jupiter's Wife (1994)
1132 | 1131,Safe (1995)
1133 | 1132,Feeling Minnesota (1996)
1134 | 1133,Escape to Witch Mountain (1975)
1135 | 1134,Get on the Bus (1996)
1136 | 1135,"Doors, The (1991)"
1137 | 1136,Ghosts of Mississippi (1996)
1138 | 1137,Beautiful Thing (1996)
1139 | 1138,Best Men (1997)
1140 | 1139,Hackers (1995)
1141 | 1140,"Road to Wellville, The (1994)"
1142 | 1141,"War Room, The (1993)"
1143 | 1142,When We Were Kings (1996)
1144 | 1143,Hard Eight (1996)
1145 | 1144,"Quiet Room, The (1996)"
1146 | 1145,Blue Chips (1994)
1147 | 1146,Calendar Girl (1993)
1148 | 1147,My Family (1995)
1149 | 1148,Tom & Viv (1994)
1150 | 1149,Walkabout (1971)
1151 | 1150,Last Dance (1996)
1152 | 1151,Original Gangstas (1996)
1153 | 1152,In Love and War (1996)
1154 | 1153,Backbeat (1993)
1155 | 1154,Alphaville (1965)
1156 | 1155,"Rendezvous in Paris (Rendez-vous de Paris, Les) (1995)"
1157 | 1156,Cyclo (1995)
1158 | 1157,"Relic, The (1997)"
1159 | 1158,"Fille seule, La (A Single Girl) (1995)"
1160 | 1159,Stalker (1979)
1161 | 1160,Love! Valour! Compassion! (1997)
1162 | 1161,Palookaville (1996)
1163 | 1162,Phat Beach (1996)
1164 | 1163,"Portrait of a Lady, The (1996)"
1165 | 1164,Zeus and Roxanne (1997)
1166 | 1165,Big Bully (1996)
1167 | 1166,Love & Human Remains (1993)
1168 | 1167,"Sum of Us, The (1994)"
1169 | 1168,Little Buddha (1993)
1170 | 1169,Fresh (1994)
1171 | 1170,Spanking the Monkey (1994)
1172 | 1171,Wild Reeds (1994)
1173 | 1172,"Women, The (1939)"
1174 | 1173,Bliss (1997)
1175 | 1174,Caught (1996)
1176 | 1175,Hugo Pool (1997)
1177 | 1176,Welcome To Sarajevo (1997)
1178 | 1177,Dunston Checks In (1996)
1179 | 1178,Major Payne (1994)
1180 | 1179,Man of the House (1995)
1181 | 1180,I Love Trouble (1994)
1182 | 1181,"Low Down Dirty Shame, A (1994)"
1183 | 1182,Cops and Robbersons (1994)
1184 | 1183,"Cowboy Way, The (1994)"
1185 | 1184,"Endless Summer 2, The (1994)"
1186 | 1185,In the Army Now (1994)
1187 | 1186,"Inkwell, The (1994)"
1188 | 1187,Switchblade Sisters (1975)
1189 | 1188,Young Guns II (1990)
1190 | 1189,Prefontaine (1997)
1191 | 1190,That Old Feeling (1997)
1192 | 1191,"Letter From Death Row, A (1998)"
1193 | 1192,"Boys of St. Vincent, The (1993)"
1194 | 1193,Before the Rain (Pred dozhdot) (1994)
1195 | 1194,Once Were Warriors (1994)
1196 | 1195,Strawberry and Chocolate (Fresa y chocolate) (1993)
1197 | 1196,"Savage Nights (Nuits fauves, Les) (1992)"
1198 | 1197,"Family Thing, A (1996)"
1199 | 1198,Purple Noon (1960)
1200 | 1199,Cemetery Man (Dellamorte Dellamore) (1994)
1201 | 1200,Kim (1950)
1202 | 1201,Marlene Dietrich: Shadow and Light (1996) 
1203 | 1202,"Maybe, Maybe Not (Bewegte Mann, Der) (1994)"
1204 | 1203,Top Hat (1935)
1205 | 1204,To Be or Not to Be (1942)
1206 | 1205,"Secret Agent, The (1996)"
1207 | 1206,Amos & Andrew (1993)
1208 | 1207,Jade (1995)
1209 | 1208,Kiss of Death (1995)
1210 | 1209,Mixed Nuts (1994)
1211 | 1210,Virtuosity (1995)
1212 | 1211,Blue Sky (1994)
1213 | 1212,Flesh and Bone (1993)
1214 | 1213,Guilty as Sin (1993)
1215 | 1214,In the Realm of the Senses (Ai no corrida) (1976)
1216 | 1215,Barb Wire (1996)
1217 | 1216,Kissed (1996)
1218 | 1217,Assassins (1995)
1219 | 1218,Friday (1995)
1220 | 1219,"Goofy Movie, A (1995)"
1221 | 1220,Higher Learning (1995)
1222 | 1221,When a Man Loves a Woman (1994)
1223 | 1222,Judgment Night (1993)
1224 | 1223,King of the Hill (1993)
1225 | 1224,"Scout, The (1994)"
1226 | 1225,Angus (1995)
1227 | 1226,Night Falls on Manhattan (1997)
1228 | 1227,"Awfully Big Adventure, An (1995)"
1229 | 1228,Under Siege 2: Dark Territory (1995)
1230 | 1229,Poison Ivy II (1995)
1231 | 1230,Ready to Wear (Pret-A-Porter) (1994)
1232 | 1231,Marked for Death (1990)
1233 | 1232,Madonna: Truth or Dare (1991)
1234 | 1233,Nénette et Boni (1996)
1235 | 1234,Chairman of the Board (1998)
1236 | 1235,"Big Bang Theory, The (1994)"
1237 | 1236,"Other Voices, Other Rooms (1997)"
1238 | 1237,Twisted (1996)
1239 | 1238,Full Speed (1996)
1240 | 1239,Cutthroat Island (1995)
1241 | 1240,Ghost in the Shell (Kokaku kidotai) (1995)
1242 | 1241,"Van, The (1996)"
1243 | 1242,"Old Lady Who Walked in the Sea, The (Vieille qui marchait dans la mer, La) (1991)"
1244 | 1243,Night Flier (1997)
1245 | 1244,Metro (1997)
1246 | 1245,Gridlock'd (1997)
1247 | 1246,Bushwhacked (1995)
1248 | 1247,Bad Girls (1994)
1249 | 1248,Blink (1994)
1250 | 1249,For Love or Money (1993)
1251 | 1250,Best of the Best 3: No Turning Back (1995)
1252 | 1251,A Chef in Love (1996)
1253 | 1252,"Contempt (Mépris, Le) (1963)"
1254 | 1253,"Tie That Binds, The (1995)"
1255 | 1254,Gone Fishin' (1997)
1256 | 1255,Broken English (1996)
1257 | 1256,"Designated Mourner, The (1997)"
1258 | 1257,"Designated Mourner, The (1997)"
1259 | 1258,Trial and Error (1997)
1260 | 1259,Pie in the Sky (1995)
1261 | 1260,Total Eclipse (1995)
1262 | 1261,"Run of the Country, The (1995)"
1263 | 1262,Walking and Talking (1996)
1264 | 1263,Foxfire (1996)
1265 | 1264,Nothing to Lose (1994)
1266 | 1265,Star Maps (1997)
1267 | 1266,Bread and Chocolate (Pane e cioccolata) (1973)
1268 | 1267,Clockers (1995)
1269 | 1268,Bitter Moon (1992)
1270 | 1269,Love in the Afternoon (1957)
1271 | 1270,Life with Mikey (1993)
1272 | 1271,North (1994)
1273 | 1272,Talking About Sex (1994)
1274 | 1273,Color of Night (1994)
1275 | 1274,Robocop 3 (1993)
1276 | 1275,Killer (Bulletproof Heart) (1994)
1277 | 1276,Sunset Park (1996)
1278 | 1277,Set It Off (1996)
1279 | 1278,Selena (1997)
1280 | 1279,Wild America (1997)
1281 | 1280,Gang Related (1997)
1282 | 1281,Manny & Lo (1996)
1283 | 1282,"Grass Harp, The (1995)"
1284 | 1283,Out to Sea (1997)
1285 | 1284,Before and After (1996)
1286 | 1285,Princess Caraboo (1994)
1287 | 1286,Shall We Dance? (1937)
1288 | 1287,Ed (1996)
1289 | 1288,Denise Calls Up (1995)
1290 | 1289,Jack and Sarah (1995)
1291 | 1290,Country Life (1994)
1292 | 1291,Celtic Pride (1996)
1293 | 1292,"Simple Wish, A (1997)"
1294 | 1293,Star Kid (1997)
1295 | 1294,Ayn Rand: A Sense of Life (1997)
1296 | 1295,Kicked in the Head (1997)
1297 | 1296,Indian Summer (1996)
1298 | 1297,Love Affair (1994)
1299 | 1298,"Band Wagon, The (1953)"
1300 | 1299,Penny Serenade (1941)
1301 | 1300,'Til There Was You (1997)
1302 | 1301,Stripes (1981)
1303 | 1302,Late Bloomers (1996)
1304 | 1303,"Getaway, The (1994)"
1305 | 1304,New York Cop (1996)
1306 | 1305,National Lampoon's Senior Trip (1995)
1307 | 1306,Delta of Venus (1994)
1308 | 1307,Carmen Miranda: Bananas Is My Business (1994)
1309 | 1308,Babyfever (1994)
1310 | 1309,"Very Natural Thing, A (1974)"
1311 | 1310,"Walk in the Sun, A (1945)"
1312 | 1311,Waiting to Exhale (1995)
1313 | 1312,"Pompatus of Love, The (1996)"
1314 | 1313,Palmetto (1998)
1315 | 1314,Surviving the Game (1994)
1316 | 1315,Inventing the Abbotts (1997)
1317 | 1316,"Horse Whisperer, The (1998)"
1318 | 1317,"Journey of August King, The (1995)"
1319 | 1318,Catwalk (1995)
1320 | 1319,"Neon Bible, The (1995)"
1321 | 1320,Homage (1995)
1322 | 1321,Open Season (1996)
1323 | 1322,Metisse (Café au Lait) (1993)
1324 | 1323,"Wooden Man's Bride, The (Wu Kui) (1994)"
1325 | 1324,Loaded (1994)
1326 | 1325,August (1996)
1327 | 1326,Boys (1996)
1328 | 1327,Captives (1994)
1329 | 1328,Of Love and Shadows (1994)
1330 | 1329,"Low Life, The (1994)"
1331 | 1330,An Unforgettable Summer (1994)
1332 | 1331,"Last Klezmer: Leopold Kozlowski, His Life and Music, The (1995)"
1333 | 1332,My Life and Times With Antonin Artaud (En compagnie d'Antonin Artaud) (1993)
1334 | 1333,Midnight Dancers (Sibak) (1994)
1335 | 1334,Somebody to Love (1994)
1336 | 1335,American Buffalo (1996)
1337 | 1336,Kazaam (1996)
1338 | 1337,Larger Than Life (1996)
1339 | 1338,Two Deaths (1995)
1340 | 1339,Stefano Quantestorie (1993)
1341 | 1340,"Crude Oasis, The (1995)"
1342 | 1341,Hedd Wyn (1992)
1343 | 1342,"Convent, The (Convento, O) (1995)"
1344 | 1343,Lotto Land (1995)
1345 | 1344,"Story of Xinghua, The (1993)"
1346 | 1345,"Day the Sun Turned Cold, The (Tianguo niezi) (1994)"
1347 | 1346,Dingo (1992)
1348 | 1347,"Ballad of Narayama, The (Narayama Bushiko) (1958)"
1349 | 1348,Every Other Weekend (1990)
1350 | 1349,Mille bolle blu (1993)
1351 | 1350,Crows and Sparrows (1949)
1352 | 1351,Lover's Knot (1996)
1353 | 1352,Shadow of Angels (Schatten der Engel) (1976)
1354 | 1353,1-900 (1994)
1355 | 1354,Venice/Venice (1992)
1356 | 1355,Infinity (1996)
1357 | 1356,Ed's Next Move (1996)
1358 | 1357,For the Moment (1994)
1359 | 1358,The Deadly Cure (1996)
1360 | 1359,Boys in Venice (1996)
1361 | 1360,"Sexual Life of the Belgians, The (1994)"
1362 | 1361,"Search for One-eye Jimmy, The (1996)"
1363 | 1362,American Strays (1996)
1364 | 1363,"Leopard Son, The (1996)"
1365 | 1364,Bird of Prey (1996)
1366 | 1365,Johnny 100 Pesos (1993)
1367 | 1366,JLG/JLG - autoportrait de décembre (1994)
1368 | 1367,Faust (1994)
1369 | 1368,Mina Tannenbaum (1994)
1370 | 1369,"Forbidden Christ, The (Cristo proibito, Il) (1950)"
1371 | 1370,I Can't Sleep (J'ai pas sommeil) (1994)
1372 | 1371,"Machine, The (1994)"
1373 | 1372,"Stranger, The (1994)"
1374 | 1373,Good Morning (1971)
1375 | 1374,Falling in Love Again (1980)
1376 | 1375,"Cement Garden, The (1993)"
1377 | 1376,Meet Wally Sparks (1997)
1378 | 1377,Hotel de Love (1996)
1379 | 1378,Rhyme & Reason (1997)
1380 | 1379,Love and Other Catastrophes (1996)
1381 | 1380,Hollow Reed (1996)
1382 | 1381,Losing Chase (1996)
1383 | 1382,"Bonheur, Le (1965)"
1384 | 1383,"Second Jungle Book: Mowgli & Baloo, The (1997)"
1385 | 1384,Squeeze (1996)
1386 | 1385,Roseanna's Grave (For Roseanna) (1997)
1387 | 1386,Tetsuo II: Body Hammer (1992)
1388 | 1387,Fall (1997)
1389 | 1388,Gabbeh (1996)
1390 | 1389,Mondo (1996)
1391 | 1390,"Innocent Sleep, The (1995)"
1392 | 1391,For Ever Mozart (1996)
1393 | 1392,"Locusts, The (1997)"
1394 | 1393,Stag (1997)
1395 | 1394,Swept from the Sea (1997)
1396 | 1395,Hurricane Streets (1998)
1397 | 1396,Stonewall (1995)
1398 | 1397,Of Human Bondage (1934)
1399 | 1398,Anna (1996)
1400 | 1399,Stranger in the House (1997)
1401 | 1400,Picture Bride (1995)
1402 | 1401,M. Butterfly (1993)
1403 | 1402,"Ciao, Professore! (1993)"
1404 | 1403,Caro Diario (Dear Diary) (1994)
1405 | 1404,Withnail and I (1987)
1406 | 1405,Boy's Life 2 (1997)
1407 | 1406,When Night Is Falling (1995)
1408 | 1407,"Specialist, The (1994)"
1409 | 1408,Gordy (1995)
1410 | 1409,"Swan Princess, The (1994)"
1411 | 1410,Harlem (1993)
1412 | 1411,Barbarella (1968)
1413 | 1412,Land Before Time III: The Time of the Great Giving (1995) (V)
1414 | 1413,Street Fighter (1994)
1415 | 1414,Coldblooded (1995)
1416 | 1415,"Next Karate Kid, The (1994)"
1417 | 1416,No Escape (1994)
1418 | 1417,"Turning, The (1992)"
1419 | 1418,"Joy Luck Club, The (1993)"
1420 | 1419,Highlander III: The Sorcerer (1994)
1421 | 1420,Gilligan's Island: The Movie (1998)
1422 | 1421,My Crazy Life (Mi vida loca) (1993)
1423 | 1422,Suture (1993)
1424 | 1423,"Walking Dead, The (1995)"
1425 | 1424,I Like It Like That (1994)
1426 | 1425,I'll Do Anything (1994)
1427 | 1426,Grace of My Heart (1996)
1428 | 1427,Drunks (1995)
1429 | 1428,SubUrbia (1997)
1430 | 1429,Sliding Doors (1998)
1431 | 1430,Ill Gotten Gains (1997)
1432 | 1431,Legal Deceit (1997)
1433 | 1432,"Mighty, The (1998)"
1434 | 1433,Men of Means (1998)
1435 | 1434,Shooting Fish (1997)
1436 | 1435,"Steal Big, Steal Little (1995)"
1437 | 1436,Mr. Jones (1993)
1438 | 1437,House Party 3 (1994)
1439 | 1438,Panther (1995)
1440 | 1439,Jason's Lyric (1994)
1441 | 1440,Above the Rim (1994)
1442 | 1441,Moonlight and Valentino (1995)
1443 | 1442,"Scarlet Letter, The (1995)"
1444 | 1443,8 Seconds (1994)
1445 | 1444,That Darn Cat! (1965)
1446 | 1445,Ladybird Ladybird (1994)
1447 | 1446,"Bye Bye, Love (1995)"
1448 | 1447,Century (1993)
1449 | 1448,My Favorite Season (1993)
1450 | 1449,Pather Panchali (1955)
1451 | 1450,Golden Earrings (1947)
1452 | 1451,Foreign Correspondent (1940)
1453 | 1452,Lady of Burlesque (1943)
1454 | 1453,Angel on My Shoulder (1946)
1455 | 1454,Angel and the Badman (1947)
1456 | 1455,"Outlaw, The (1943)"
1457 | 1456,Beat the Devil (1954)
1458 | 1457,Love Is All There Is (1996)
1459 | 1458,"Damsel in Distress, A (1937)"
1460 | 1459,Madame Butterfly (1995)
1461 | 1460,Sleepover (1995)
1462 | 1461,Here Comes Cookie (1935)
1463 | 1462,"Thieves (Voleurs, Les) (1996)"
1464 | 1463,"Boys, Les (1997)"
1465 | 1464,"Stars Fell on Henrietta, The (1995)"
1466 | 1465,Last Summer in the Hamptons (1995)
1467 | 1466,Margaret's Museum (1995)
1468 | 1467,"Saint of Fort Washington, The (1993)"
1469 | 1468,"Cure, The (1995)"
1470 | 1469,Tom and Huck (1995)
1471 | 1470,Gumby: The Movie (1995)
1472 | 1471,Hideaway (1995)
1473 | 1472,"Visitors, The (Visiteurs, Les) (1993)"
1474 | 1473,"Little Princess, The (1939)"
1475 | 1474,Nina Takes a Lover (1994)
1476 | 1475,Bhaji on the Beach (1993)
1477 | 1476,Raw Deal (1948)
1478 | 1477,Nightwatch (1997)
1479 | 1478,Dead Presidents (1995)
1480 | 1479,Reckless (1995)
1481 | 1480,Herbie Rides Again (1974)
1482 | 1481,S.F.W. (1994)
1483 | 1482,"Gate of Heavenly Peace, The (1995)"
1484 | 1483,"Man in the Iron Mask, The (1998)"
1485 | 1484,"Jerky Boys, The (1994)"
1486 | 1485,"Colonel Chabert, Le (1994)"
1487 | 1486,Girl in the Cadillac (1995)
1488 | 1487,Even Cowgirls Get the Blues (1993)
1489 | 1488,Germinal (1993)
1490 | 1489,Chasers (1994)
1491 | 1490,Fausto (1993)
1492 | 1491,Tough and Deadly (1995)
1493 | 1492,Window to Paris (1994)
1494 | 1493,"Modern Affair, A (1995)"
1495 | 1494,"Mostro, Il (1994)"
1496 | 1495,Flirt (1995)
1497 | 1496,Carpool (1996)
1498 | 1497,"Line King: Al Hirschfeld, The (1996)"
1499 | 1498,Farmer & Chase (1995)
1500 | 1499,Grosse Fatigue (1994)
1501 | 1500,Santa with Muscles (1996)
1502 | 1501,Prisoner of the Mountains (Kavkazsky Plennik) (1996)
1503 | 1502,Naked in New York (1994)
1504 | 1503,Gold Diggers: The Secret of Bear Mountain (1995)
1505 | 1504,"Bewegte Mann, Der (1994)"
1506 | 1505,Killer: A Journal of Murder (1995)
1507 | 1506,Nelly & Monsieur Arnaud (1995)
1508 | 1507,Three Lives and Only One Death (1996)
1509 | 1508,"Babysitter, The (1995)"
1510 | 1509,Getting Even with Dad (1994)
1511 | 1510,Mad Dog Time (1996)
1512 | 1511,Children of the Revolution (1996)
1513 | 1512,"World of Apu, The (Apur Sansar) (1959)"
1514 | 1513,Sprung (1997)
1515 | 1514,Dream With the Fishes (1997)
1516 | 1515,Wings of Courage (1995)
1517 | 1516,"Wedding Gift, The (1994)"
1518 | 1517,Race the Sun (1996)
1519 | 1518,Losing Isaiah (1995)
1520 | 1519,New Jersey Drive (1995)
1521 | 1520,"Fear, The (1995)"
1522 | 1521,Mr. Wonderful (1993)
1523 | 1522,Trial by Jury (1994)
1524 | 1523,"Good Man in Africa, A (1994)"
1525 | 1524,Kaspar Hauser (1993)
1526 | 1525,"Object of My Affection, The (1998)"
1527 | 1526,Witness (1985)
1528 | 1527,Senseless (1998)
1529 | 1528,Nowhere (1997)
1530 | 1529,Underground (1995)
1531 | 1530,Jefferson in Paris (1995)
1532 | 1531,Far From Home: The Adventures of Yellow Dog (1995)
1533 | 1532,Foreign Student (1994)
1534 | 1533,I Don't Want to Talk About It (De eso no se habla) (1993)
1535 | 1534,Twin Town (1997)
1536 | 1535,"Enfer, L' (1994)"
1537 | 1536,Aiqing wansui (1994)
1538 | 1537,Cosi (1996)
1539 | 1538,All Over Me (1997)
1540 | 1539,Being Human (1993)
1541 | 1540,"Amazing Panda Adventure, The (1995)"
1542 | 1541,"Beans of Egypt, Maine, The (1994)"
1543 | 1542,"Scarlet Letter, The (1926)"
1544 | 1543,Johns (1996)
1545 | 1544,It Takes Two (1995)
1546 | 1545,Frankie Starlight (1995)
1547 | 1546,Shadows (Cienie) (1988)
1548 | 1547,"Show, The (1995)"
1549 | 1548,The Courtyard (1995)
1550 | 1549,Dream Man (1995)
1551 | 1550,Destiny Turns on the Radio (1995)
1552 | 1551,"Glass Shield, The (1994)"
1553 | 1552,"Hunted, The (1995)"
1554 | 1553,"Underneath, The (1995)"
1555 | 1554,Safe Passage (1994)
1556 | 1555,"Secret Adventures of Tom Thumb, The (1993)"
1557 | 1556,Condition Red (1995)
1558 | 1557,Yankee Zulu (1994)
1559 | 1558,Aparajito (1956)
1560 | 1559,Hostile Intentions (1994)
1561 | 1560,Clean Slate (Coup de Torchon) (1981)
1562 | 1561,Tigrero: A Film That Was Never Made (1994)
1563 | 1562,"Eye of Vichy, The (Oeil de Vichy, L') (1993)"
1564 | 1563,"Promise, The (Versprechen, Das) (1994)"
1565 | 1564,To Cross the Rubicon (1991)
1566 | 1565,Daens (1992)
1567 | 1566,"Man from Down Under, The (1943)"
1568 | 1567,Careful (1992)
1569 | 1568,Vermont Is For Lovers (1992)
1570 | 1569,"Vie est belle, La (Life is Rosey) (1987)"
1571 | 1570,Quartier Mozart (1992)
1572 | 1571,Touki Bouki (Journey of the Hyena) (1973)
1573 | 1572,Wend Kuuni (God's Gift) (1982)
1574 | 1573,Spirits of the Dead (Tre passi nel delirio) (1968)
1575 | 1574,Pharaoh's Army (1995)
1576 | 1575,"I, Worst of All (Yo, la peor de todas) (1990)"
1577 | 1576,"Hungarian Fairy Tale, A (1987)"
1578 | 1577,"Death in the Garden (Mort en ce jardin, La) (1956)"
1579 | 1578,"Collectionneuse, La (1967)"
1580 | 1579,Baton Rouge (1988)
1581 | 1580,Liebelei (1933)
1582 | 1581,"Woman in Question, The (1950)"
1583 | 1582,T-Men (1947)
1584 | 1583,"Invitation, The (Zaproszenie) (1986)"
1585 | 1584,"Symphonie pastorale, La (1946)"
1586 | 1585,American Dream (1990)
1587 | 1586,Lashou shentan (1992)
1588 | 1587,Terror in a Texas Town (1958)
1589 | 1588,Salut cousin! (1996)
1590 | 1589,Schizopolis (1996)
1591 | 1590,"To Have, or Not (1995)"
1592 | 1591,Duoluo tianshi (1995)
1593 | 1592,"Magic Hour, The (1998)"
1594 | 1593,Death in Brunswick (1991)
1595 | 1594,Everest (1998)
1596 | 1595,Shopping (1994)
1597 | 1596,Nemesis 2: Nebula (1995)
1598 | 1597,Romper Stomper (1992)
1599 | 1598,City of Industry (1997)
1600 | 1599,Someone Else's America (1995)
1601 | 1600,Guantanamera (1994)
1602 | 1601,Office Killer (1997)
1603 | 1602,"Price Above Rubies, A (1998)"
1604 | 1603,Angela (1995)
1605 | 1604,He Walked by Night (1948)
1606 | 1605,Love Serenade (1996)
1607 | 1606,Deceiver (1997)
1608 | 1607,Hurricane Streets (1998)
1609 | 1608,Buddy (1997)
1610 | 1609,B*A*P*S (1997)
1611 | 1610,"Truth or Consequences, N.M. (1997)"
1612 | 1611,Intimate Relations (1996)
1613 | 1612,"Leading Man, The (1996)"
1614 | 1613,Tokyo Fist (1995)
1615 | 1614,"Reluctant Debutante, The (1958)"
1616 | 1615,Warriors of Virtue (1997)
1617 | 1616,Desert Winds (1995)
1618 | 1617,Hugo Pool (1997)
1619 | 1618,King of New York (1990)
1620 | 1619,All Things Fair (1996)
1621 | 1620,"Sixth Man, The (1997)"
1622 | 1621,Butterfly Kiss (1995)
1623 | 1622,"Paris, France (1993)"
1624 | 1623,"Cérémonie, La (1995)"
1625 | 1624,Hush (1998)
1626 | 1625,Nightwatch (1997)
1627 | 1626,Nobody Loves Me (Keiner liebt mich) (1994)
1628 | 1627,"Wife, The (1995)"
1629 | 1628,Lamerica (1994)
1630 | 1629,Nico Icon (1995)
1631 | 1630,"Silence of the Palace, The (Saimt el Qusur) (1994)"
1632 | 1631,"Slingshot, The (1993)"
1633 | 1632,Land and Freedom (Tierra y libertad) (1995)
1634 | 1633,Á köldum klaka (Cold Fever) (1994)
1635 | 1634,Etz Hadomim Tafus (Under the Domin Tree) (1994)
1636 | 1635,Two Friends (1986) 
1637 | 1636,Brothers in Trouble (1995)
1638 | 1637,Girls Town (1996)
1639 | 1638,Normal Life (1996)
1640 | 1639,Bitter Sugar (Azucar Amargo) (1996)
1641 | 1640,"Eighth Day, The (1996)"
1642 | 1641,Dadetown (1995)
1643 | 1642,Some Mother's Son (1996)
1644 | 1643,Angel Baby (1995)
1645 | 1644,Sudden Manhattan (1996)
1646 | 1645,"Butcher Boy, The (1998)"
1647 | 1646,Men With Guns (1997)
1648 | 1647,Hana-bi (1997)
1649 | 1648,"Niagara, Niagara (1997)"
1650 | 1649,"Big One, The (1997)"
1651 | 1650,"Butcher Boy, The (1998)"
1652 | 1651,"Spanish Prisoner, The (1997)"
1653 | 1652,Temptress Moon (Feng Yue) (1996)
1654 | 1653,Entertaining Angels: The Dorothy Day Story (1996)
1655 | 1654,Chairman of the Board (1998)
1656 | 1655,"Favor, The (1994)"
1657 | 1656,Little City (1998)
1658 | 1657,Target (1995)
1659 | 1658,"Substance of Fire, The (1996)"
1660 | 1659,Getting Away With Murder (1996)
1661 | 1660,Small Faces (1995)
1662 | 1661,"New Age, The (1994)"
1663 | 1662,Rough Magic (1995)
1664 | 1663,Nothing Personal (1995)
1665 | 1664,8 Heads in a Duffel Bag (1997)
1666 | 1665,"Brother's Kiss, A (1997)"
1667 | 1666,Ripe (1996)
1668 | 1667,"Next Step, The (1995)"
1669 | 1668,Wedding Bell Blues (1996)
1670 | 1669,MURDER and murder (1996)
1671 | 1670,Tainted (1998)
1672 | 1671,"Further Gesture, A (1996)"
1673 | 1672,Kika (1993)
1674 | 1673,Mirage (1995)
1675 | 1674,Mamma Roma (1962)
1676 | 1675,"Sunchaser, The (1996)"
1677 | 1676,"War at Home, The (1996)"
1678 | 1677,Sweet Nothing (1995)
1679 | 1678,Mat' i syn (1997)
1680 | 1679,B. Monkey (1998)
1681 | 1680,Sliding Doors (1998)
1682 | 1681,You So Crazy (1994)
1683 | 1682,Scream of Stone (Schrei aus Stein) (1991)
1684 | 


--------------------------------------------------------------------------------
/MultipleLinearRegression.ipynb:
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  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "code",
  5 |    "execution_count": 2,
  6 |    "id": "6ddb1a51",
  7 |    "metadata": {
  8 |     "scrolled": true
  9 |    },
 10 |    "outputs": [
 11 |     {
 12 |      "data": {
 13 |       "text/html": [
 14 |        "<div>\n",
 15 |        "<style scoped>\n",
 16 |        "    .dataframe tbody tr th:only-of-type {\n",
 17 |        "        vertical-align: middle;\n",
 18 |        "    }\n",
 19 |        "\n",
 20 |        "    .dataframe tbody tr th {\n",
 21 |        "        vertical-align: top;\n",
 22 |        "    }\n",
 23 |        "\n",
 24 |        "    .dataframe thead th {\n",
 25 |        "        text-align: right;\n",
 26 |        "    }\n",
 27 |        "</style>\n",
 28 |        "<table border=\"1\" class=\"dataframe\">\n",
 29 |        "  <thead>\n",
 30 |        "    <tr style=\"text-align: right;\">\n",
 31 |        "      <th></th>\n",
 32 |        "      <th>R&amp;D Spend</th>\n",
 33 |        "      <th>Administration</th>\n",
 34 |        "      <th>Marketing Spend</th>\n",
 35 |        "      <th>State</th>\n",
 36 |        "      <th>Profit</th>\n",
 37 |        "    </tr>\n",
 38 |        "  </thead>\n",
 39 |        "  <tbody>\n",
 40 |        "    <tr>\n",
 41 |        "      <th>0</th>\n",
 42 |        "      <td>165349.20</td>\n",
 43 |        "      <td>136897.80</td>\n",
 44 |        "      <td>471784.10</td>\n",
 45 |        "      <td>New York</td>\n",
 46 |        "      <td>192261.83</td>\n",
 47 |        "    </tr>\n",
 48 |        "    <tr>\n",
 49 |        "      <th>1</th>\n",
 50 |        "      <td>162597.70</td>\n",
 51 |        "      <td>151377.59</td>\n",
 52 |        "      <td>443898.53</td>\n",
 53 |        "      <td>California</td>\n",
 54 |        "      <td>191792.06</td>\n",
 55 |        "    </tr>\n",
 56 |        "    <tr>\n",
 57 |        "      <th>2</th>\n",
 58 |        "      <td>153441.51</td>\n",
 59 |        "      <td>101145.55</td>\n",
 60 |        "      <td>407934.54</td>\n",
 61 |        "      <td>Florida</td>\n",
 62 |        "      <td>191050.39</td>\n",
 63 |        "    </tr>\n",
 64 |        "    <tr>\n",
 65 |        "      <th>3</th>\n",
 66 |        "      <td>144372.41</td>\n",
 67 |        "      <td>118671.85</td>\n",
 68 |        "      <td>383199.62</td>\n",
 69 |        "      <td>New York</td>\n",
 70 |        "      <td>182901.99</td>\n",
 71 |        "    </tr>\n",
 72 |        "    <tr>\n",
 73 |        "      <th>4</th>\n",
 74 |        "      <td>142107.34</td>\n",
 75 |        "      <td>91391.77</td>\n",
 76 |        "      <td>366168.42</td>\n",
 77 |        "      <td>Florida</td>\n",
 78 |        "      <td>166187.94</td>\n",
 79 |        "    </tr>\n",
 80 |        "  </tbody>\n",
 81 |        "</table>\n",
 82 |        "</div>"
 83 |       ],
 84 |       "text/plain": [
 85 |        "   R&D Spend  Administration  Marketing Spend       State     Profit\n",
 86 |        "0  165349.20       136897.80        471784.10    New York  192261.83\n",
 87 |        "1  162597.70       151377.59        443898.53  California  191792.06\n",
 88 |        "2  153441.51       101145.55        407934.54     Florida  191050.39\n",
 89 |        "3  144372.41       118671.85        383199.62    New York  182901.99\n",
 90 |        "4  142107.34        91391.77        366168.42     Florida  166187.94"
 91 |       ]
 92 |      },
 93 |      "execution_count": 2,
 94 |      "metadata": {},
 95 |      "output_type": "execute_result"
 96 |     }
 97 |    ],
 98 |    "source": [
 99 |     "#import the libraries\n",
100 |     "import numpy as np\n",
101 |     "import matplotlib.pyplot as plt\n",
102 |     "import pandas as pd\n",
103 |     "\n",
104 |     "#reading the dataset\n",
105 |     "dataset = pd.read_csv('50_Startups.csv')\n",
106 |     "X = dataset.iloc[:, :-1].values\n",
107 |     "y = dataset.iloc[:, -1].values\n",
108 |     "dataset.head(5)"
109 |    ]
110 |   },
111 |   {
112 |    "cell_type": "code",
113 |    "execution_count": 3,
114 |    "id": "16b28f9a",
115 |    "metadata": {
116 |     "scrolled": true
117 |    },
118 |    "outputs": [],
119 |    "source": [
120 |     "from sklearn.compose import ColumnTransformer\n",
121 |     "from sklearn.preprocessing import OneHotEncoder\n",
122 |     "ct = ColumnTransformer(transformers=[('encoder', OneHotEncoder(), [3])], remainder='passthrough')\n",
123 |     "X = np.array(ct.fit_transform(X))\n"
124 |    ]
125 |   },
126 |   {
127 |    "cell_type": "code",
128 |    "execution_count": 4,
129 |    "id": "a1434493",
130 |    "metadata": {},
131 |    "outputs": [],
132 |    "source": [
133 |     "from sklearn.model_selection import train_test_split\n",
134 |     "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2)"
135 |    ]
136 |   },
137 |   {
138 |    "cell_type": "code",
139 |    "execution_count": 5,
140 |    "id": "baa2281b",
141 |    "metadata": {},
142 |    "outputs": [
143 |     {
144 |      "data": {
145 |       "text/plain": [
146 |        "LinearRegression()"
147 |       ]
148 |      },
149 |      "execution_count": 5,
150 |      "metadata": {},
151 |      "output_type": "execute_result"
152 |     }
153 |    ],
154 |    "source": [
155 |     "from sklearn.linear_model import LinearRegression\n",
156 |     "regressor = LinearRegression()\n",
157 |     "regressor.fit(X_train, y_train)"
158 |    ]
159 |   },
160 |   {
161 |    "cell_type": "code",
162 |    "execution_count": 6,
163 |    "id": "1e03e915",
164 |    "metadata": {},
165 |    "outputs": [],
166 |    "source": [
167 |     "y_pred = regressor.predict(X_test)"
168 |    ]
169 |   },
170 |   {
171 |    "cell_type": "code",
172 |    "execution_count": 7,
173 |    "id": "d1612497",
174 |    "metadata": {},
175 |    "outputs": [
176 |     {
177 |      "data": {
178 |       "text/html": [
179 |        "<div>\n",
180 |        "<style scoped>\n",
181 |        "    .dataframe tbody tr th:only-of-type {\n",
182 |        "        vertical-align: middle;\n",
183 |        "    }\n",
184 |        "\n",
185 |        "    .dataframe tbody tr th {\n",
186 |        "        vertical-align: top;\n",
187 |        "    }\n",
188 |        "\n",
189 |        "    .dataframe thead th {\n",
190 |        "        text-align: right;\n",
191 |        "    }\n",
192 |        "</style>\n",
193 |        "<table border=\"1\" class=\"dataframe\">\n",
194 |        "  <thead>\n",
195 |        "    <tr style=\"text-align: right;\">\n",
196 |        "      <th></th>\n",
197 |        "      <th>Real Values</th>\n",
198 |        "      <th>Predicted Values</th>\n",
199 |        "    </tr>\n",
200 |        "  </thead>\n",
201 |        "  <tbody>\n",
202 |        "    <tr>\n",
203 |        "      <th>0</th>\n",
204 |        "      <td>155752.60</td>\n",
205 |        "      <td>162085.051813</td>\n",
206 |        "    </tr>\n",
207 |        "    <tr>\n",
208 |        "      <th>1</th>\n",
209 |        "      <td>146121.95</td>\n",
210 |        "      <td>137621.884189</td>\n",
211 |        "    </tr>\n",
212 |        "    <tr>\n",
213 |        "      <th>2</th>\n",
214 |        "      <td>192261.83</td>\n",
215 |        "      <td>190067.226260</td>\n",
216 |        "    </tr>\n",
217 |        "    <tr>\n",
218 |        "      <th>3</th>\n",
219 |        "      <td>96778.92</td>\n",
220 |        "      <td>100662.330085</td>\n",
221 |        "    </tr>\n",
222 |        "    <tr>\n",
223 |        "      <th>4</th>\n",
224 |        "      <td>99937.59</td>\n",
225 |        "      <td>102927.822474</td>\n",
226 |        "    </tr>\n",
227 |        "    <tr>\n",
228 |        "      <th>5</th>\n",
229 |        "      <td>105733.54</td>\n",
230 |        "      <td>114343.551214</td>\n",
231 |        "    </tr>\n",
232 |        "    <tr>\n",
233 |        "      <th>6</th>\n",
234 |        "      <td>81005.76</td>\n",
235 |        "      <td>85736.312989</td>\n",
236 |        "    </tr>\n",
237 |        "    <tr>\n",
238 |        "      <th>7</th>\n",
239 |        "      <td>110352.25</td>\n",
240 |        "      <td>117233.468264</td>\n",
241 |        "    </tr>\n",
242 |        "    <tr>\n",
243 |        "      <th>8</th>\n",
244 |        "      <td>14681.40</td>\n",
245 |        "      <td>52207.411731</td>\n",
246 |        "    </tr>\n",
247 |        "    <tr>\n",
248 |        "      <th>9</th>\n",
249 |        "      <td>132602.65</td>\n",
250 |        "      <td>152331.104809</td>\n",
251 |        "    </tr>\n",
252 |        "  </tbody>\n",
253 |        "</table>\n",
254 |        "</div>"
255 |       ],
256 |       "text/plain": [
257 |        "   Real Values  Predicted Values\n",
258 |        "0    155752.60     162085.051813\n",
259 |        "1    146121.95     137621.884189\n",
260 |        "2    192261.83     190067.226260\n",
261 |        "3     96778.92     100662.330085\n",
262 |        "4     99937.59     102927.822474\n",
263 |        "5    105733.54     114343.551214\n",
264 |        "6     81005.76      85736.312989\n",
265 |        "7    110352.25     117233.468264\n",
266 |        "8     14681.40      52207.411731\n",
267 |        "9    132602.65     152331.104809"
268 |       ]
269 |      },
270 |      "execution_count": 7,
271 |      "metadata": {},
272 |      "output_type": "execute_result"
273 |     }
274 |    ],
275 |    "source": [
276 |     "df = pd.DataFrame({'Real Values':y_test, 'Predicted Values':y_pred})\n",
277 |     "df"
278 |    ]
279 |   },
280 |   {
281 |    "cell_type": "code",
282 |    "execution_count": 8,
283 |    "id": "a3ca2230",
284 |    "metadata": {},
285 |    "outputs": [
286 |     {
287 |      "name": "stdout",
288 |      "output_type": "stream",
289 |      "text": [
290 |       "14430.747692019655\n"
291 |      ]
292 |     }
293 |    ],
294 |    "source": [
295 |     "# RMSE\n",
296 |     "from sklearn import metrics\n",
297 |     "print(np.sqrt(metrics.mean_squared_error(y_test, y_pred)))"
298 |    ]
299 |   },
300 |   {
301 |    "cell_type": "code",
302 |    "execution_count": null,
303 |    "id": "589a20a8",
304 |    "metadata": {},
305 |    "outputs": [],
306 |    "source": []
307 |   }
308 |  ],
309 |  "metadata": {
310 |   "kernelspec": {
311 |    "display_name": "Python 3",
312 |    "language": "python",
313 |    "name": "python3"
314 |   },
315 |   "language_info": {
316 |    "codemirror_mode": {
317 |     "name": "ipython",
318 |     "version": 3
319 |    },
320 |    "file_extension": ".py",
321 |    "mimetype": "text/x-python",
322 |    "name": "python",
323 |    "nbconvert_exporter": "python",
324 |    "pygments_lexer": "ipython3",
325 |    "version": "3.8.8"
326 |   }
327 |  },
328 |  "nbformat": 4,
329 |  "nbformat_minor": 5
330 | }
331 | 


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/README.md:
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1 | 
2 | 
3 | 
4 | # Python-for-Machine-Learning---The-Complete-Beginner-s-Course


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/homeprices.csv:
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1 | Area,Price
2 | 2600,550000
3 | 3000,565000
4 | 3200,610000
5 | 3600,680000
6 | 4000,725000
7 | 


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/linear_regression_houseprice.ipynb:
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  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "code",
  5 |    "execution_count": 1,
  6 |    "id": "6f74d316",
  7 |    "metadata": {},
  8 |    "outputs": [],
  9 |    "source": [
 10 |     "import pandas as pd\n",
 11 |     "import numpy as np\n",
 12 |     "import matplotlib.pyplot as plt\n",
 13 |     "from sklearn.linear_model import LinearRegression"
 14 |    ]
 15 |   },
 16 |   {
 17 |    "cell_type": "code",
 18 |    "execution_count": 2,
 19 |    "id": "0993f3d3",
 20 |    "metadata": {},
 21 |    "outputs": [
 22 |     {
 23 |      "data": {
 24 |       "text/html": [
 25 |        "<div>\n",
 26 |        "<style scoped>\n",
 27 |        "    .dataframe tbody tr th:only-of-type {\n",
 28 |        "        vertical-align: middle;\n",
 29 |        "    }\n",
 30 |        "\n",
 31 |        "    .dataframe tbody tr th {\n",
 32 |        "        vertical-align: top;\n",
 33 |        "    }\n",
 34 |        "\n",
 35 |        "    .dataframe thead th {\n",
 36 |        "        text-align: right;\n",
 37 |        "    }\n",
 38 |        "</style>\n",
 39 |        "<table border=\"1\" class=\"dataframe\">\n",
 40 |        "  <thead>\n",
 41 |        "    <tr style=\"text-align: right;\">\n",
 42 |        "      <th></th>\n",
 43 |        "      <th>Area</th>\n",
 44 |        "      <th>Price</th>\n",
 45 |        "    </tr>\n",
 46 |        "  </thead>\n",
 47 |        "  <tbody>\n",
 48 |        "    <tr>\n",
 49 |        "      <th>0</th>\n",
 50 |        "      <td>2600</td>\n",
 51 |        "      <td>550000</td>\n",
 52 |        "    </tr>\n",
 53 |        "    <tr>\n",
 54 |        "      <th>1</th>\n",
 55 |        "      <td>3000</td>\n",
 56 |        "      <td>565000</td>\n",
 57 |        "    </tr>\n",
 58 |        "    <tr>\n",
 59 |        "      <th>2</th>\n",
 60 |        "      <td>3200</td>\n",
 61 |        "      <td>610000</td>\n",
 62 |        "    </tr>\n",
 63 |        "    <tr>\n",
 64 |        "      <th>3</th>\n",
 65 |        "      <td>3600</td>\n",
 66 |        "      <td>680000</td>\n",
 67 |        "    </tr>\n",
 68 |        "    <tr>\n",
 69 |        "      <th>4</th>\n",
 70 |        "      <td>4000</td>\n",
 71 |        "      <td>725000</td>\n",
 72 |        "    </tr>\n",
 73 |        "  </tbody>\n",
 74 |        "</table>\n",
 75 |        "</div>"
 76 |       ],
 77 |       "text/plain": [
 78 |        "   Area   Price\n",
 79 |        "0  2600  550000\n",
 80 |        "1  3000  565000\n",
 81 |        "2  3200  610000\n",
 82 |        "3  3600  680000\n",
 83 |        "4  4000  725000"
 84 |       ]
 85 |      },
 86 |      "execution_count": 2,
 87 |      "metadata": {},
 88 |      "output_type": "execute_result"
 89 |     }
 90 |    ],
 91 |    "source": [
 92 |     "df = pd.read_csv(\"homeprices.csv\")\n",
 93 |     "df"
 94 |    ]
 95 |   },
 96 |   {
 97 |    "cell_type": "code",
 98 |    "execution_count": 3,
 99 |    "id": "0c0fa991",
100 |    "metadata": {},
101 |    "outputs": [
102 |     {
103 |      "data": {
104 |       "text/plain": [
105 |        "<matplotlib.collections.PathCollection at 0x2a2b538cee0>"
106 |       ]
107 |      },
108 |      "execution_count": 3,
109 |      "metadata": {},
110 |      "output_type": "execute_result"
111 |     },
112 |     {
113 |      "data": {
114 |       "image/png": 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\n",
115 |       "text/plain": [
116 |        "<Figure size 432x288 with 1 Axes>"
117 |       ]
118 |      },
119 |      "metadata": {
120 |       "needs_background": "light"
121 |      },
122 |      "output_type": "display_data"
123 |     }
124 |    ],
125 |    "source": [
126 |     "plt.xlabel('Area(sqr ft)')\n",
127 |     "plt.ylabel('Price(US$)')\n",
128 |     "plt.scatter(df.Area, df.Price, color = 'red', marker='+')"
129 |    ]
130 |   },
131 |   {
132 |    "cell_type": "code",
133 |    "execution_count": 4,
134 |    "id": "52cc86e4",
135 |    "metadata": {},
136 |    "outputs": [
137 |     {
138 |      "data": {
139 |       "text/plain": [
140 |        "LinearRegression()"
141 |       ]
142 |      },
143 |      "execution_count": 4,
144 |      "metadata": {},
145 |      "output_type": "execute_result"
146 |     }
147 |    ],
148 |    "source": [
149 |     "reg = LinearRegression()\n",
150 |     "reg.fit(df[['Area']], df.Price)"
151 |    ]
152 |   },
153 |   {
154 |    "cell_type": "code",
155 |    "execution_count": 5,
156 |    "id": "0683d1c9",
157 |    "metadata": {},
158 |    "outputs": [
159 |     {
160 |      "data": {
161 |       "text/plain": [
162 |        "array([628715.75342466])"
163 |       ]
164 |      },
165 |      "execution_count": 5,
166 |      "metadata": {},
167 |      "output_type": "execute_result"
168 |     }
169 |    ],
170 |    "source": [
171 |     "reg.predict([[3300]])"
172 |    ]
173 |   },
174 |   {
175 |    "cell_type": "code",
176 |    "execution_count": 6,
177 |    "id": "de657f27",
178 |    "metadata": {},
179 |    "outputs": [
180 |     {
181 |      "data": {
182 |       "text/plain": [
183 |        "180616.43835616432"
184 |       ]
185 |      },
186 |      "execution_count": 6,
187 |      "metadata": {},
188 |      "output_type": "execute_result"
189 |     }
190 |    ],
191 |    "source": [
192 |     "reg.intercept_"
193 |    ]
194 |   },
195 |   {
196 |    "cell_type": "code",
197 |    "execution_count": 7,
198 |    "id": "7161a6a0",
199 |    "metadata": {},
200 |    "outputs": [
201 |     {
202 |      "data": {
203 |       "text/plain": [
204 |        "array([135.78767123])"
205 |       ]
206 |      },
207 |      "execution_count": 7,
208 |      "metadata": {},
209 |      "output_type": "execute_result"
210 |     }
211 |    ],
212 |    "source": [
213 |     "reg.coef_"
214 |    ]
215 |   },
216 |   {
217 |    "cell_type": "code",
218 |    "execution_count": 8,
219 |    "id": "c442387c",
220 |    "metadata": {},
221 |    "outputs": [
222 |     {
223 |      "data": {
224 |       "text/plain": [
225 |        "628716.838"
226 |       ]
227 |      },
228 |      "execution_count": 8,
229 |      "metadata": {},
230 |      "output_type": "execute_result"
231 |     }
232 |    ],
233 |    "source": [
234 |     "135.788 * 3300 + 180616.438"
235 |    ]
236 |   },
237 |   {
238 |    "cell_type": "code",
239 |    "execution_count": null,
240 |    "id": "3d8aa840",
241 |    "metadata": {},
242 |    "outputs": [],
243 |    "source": []
244 |   }
245 |  ],
246 |  "metadata": {
247 |   "kernelspec": {
248 |    "display_name": "Python 3",
249 |    "language": "python",
250 |    "name": "python3"
251 |   },
252 |   "language_info": {
253 |    "codemirror_mode": {
254 |     "name": "ipython",
255 |     "version": 3
256 |    },
257 |    "file_extension": ".py",
258 |    "mimetype": "text/x-python",
259 |    "name": "python",
260 |    "nbconvert_exporter": "python",
261 |    "pygments_lexer": "ipython3",
262 |    "version": "3.8.8"
263 |   }
264 |  },
265 |  "nbformat": 4,
266 |  "nbformat_minor": 5
267 | }
268 | 


--------------------------------------------------------------------------------
/logistic_regression_Binary_Classification.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "code",
  5 |    "execution_count": 1,
  6 |    "id": "56ab457f",
  7 |    "metadata": {},
  8 |    "outputs": [],
  9 |    "source": [
 10 |     "#Importing the libraries\n",
 11 |     "import numpy as np\n",
 12 |     "import matplotlib.pyplot as plt\n",
 13 |     "import pandas as pd\n",
 14 |     "from sklearn.metrics import confusion_matrix"
 15 |    ]
 16 |   },
 17 |   {
 18 |    "cell_type": "code",
 19 |    "execution_count": 2,
 20 |    "id": "2438c0eb",
 21 |    "metadata": {},
 22 |    "outputs": [],
 23 |    "source": [
 24 |     "# Importing the dataset\n",
 25 |     "dataset = pd.read_csv('user data.csv')\n",
 26 |     "X = dataset.iloc[:, [2,4]].values\n",
 27 |     "y = dataset.iloc[:, 4].values"
 28 |    ]
 29 |   },
 30 |   {
 31 |    "cell_type": "code",
 32 |    "execution_count": 3,
 33 |    "id": "14eebe92",
 34 |    "metadata": {},
 35 |    "outputs": [],
 36 |    "source": [
 37 |     "#Training and Testing Data (divide the data into two part)\n",
 38 |     "from sklearn.model_selection import train_test_split\n",
 39 |     "X_train, X_test, y_train, y_test =train_test_split(X,y,test_size= 0.25, random_state=0)"
 40 |    ]
 41 |   },
 42 |   {
 43 |    "cell_type": "code",
 44 |    "execution_count": 4,
 45 |    "id": "952c4e44",
 46 |    "metadata": {},
 47 |    "outputs": [],
 48 |    "source": [
 49 |     "from sklearn.preprocessing import StandardScaler\n",
 50 |     "sc_X = StandardScaler()\n",
 51 |     "X_train = sc_X.fit_transform(X_train)\n",
 52 |     "X_test = sc_X.fit_transform(X_test)"
 53 |    ]
 54 |   },
 55 |   {
 56 |    "cell_type": "code",
 57 |    "execution_count": 8,
 58 |    "id": "6ae4bddc",
 59 |    "metadata": {},
 60 |    "outputs": [
 61 |     {
 62 |      "data": {
 63 |       "text/plain": [
 64 |        "LogisticRegression(random_state=0)"
 65 |       ]
 66 |      },
 67 |      "execution_count": 8,
 68 |      "metadata": {},
 69 |      "output_type": "execute_result"
 70 |     }
 71 |    ],
 72 |    "source": [
 73 |     "from sklearn.linear_model import LogisticRegression\n",
 74 |     "classifer=LogisticRegression(random_state=0)\n",
 75 |     "classifer.fit(X_train,y_train)"
 76 |    ]
 77 |   },
 78 |   {
 79 |    "cell_type": "code",
 80 |    "execution_count": 9,
 81 |    "id": "23fd4aec",
 82 |    "metadata": {
 83 |     "scrolled": true
 84 |    },
 85 |    "outputs": [
 86 |     {
 87 |      "name": "stdout",
 88 |      "output_type": "stream",
 89 |      "text": [
 90 |       "[[68  0]\n",
 91 |       " [ 0 32]]\n"
 92 |      ]
 93 |     }
 94 |    ],
 95 |    "source": [
 96 |     "y_pred = classifer.predict(X_test)\n",
 97 |     "\n",
 98 |     "# Making the Confusion Matrix\n",
 99 |     "cm = confusion_matrix(y_pred, y_test)\n",
100 |     "print(cm)"
101 |    ]
102 |   },
103 |   {
104 |    "cell_type": "code",
105 |    "execution_count": null,
106 |    "id": "fca8cace",
107 |    "metadata": {},
108 |    "outputs": [],
109 |    "source": []
110 |   }
111 |  ],
112 |  "metadata": {
113 |   "kernelspec": {
114 |    "display_name": "Python 3",
115 |    "language": "python",
116 |    "name": "python3"
117 |   },
118 |   "language_info": {
119 |    "codemirror_mode": {
120 |     "name": "ipython",
121 |     "version": 3
122 |    },
123 |    "file_extension": ".py",
124 |    "mimetype": "text/x-python",
125 |    "name": "python",
126 |    "nbconvert_exporter": "python",
127 |    "pygments_lexer": "ipython3",
128 |    "version": "3.8.8"
129 |   }
130 |  },
131 |  "nbformat": 4,
132 |  "nbformat_minor": 5
133 | }
134 | 


--------------------------------------------------------------------------------
/mall+customers+data.csv:
--------------------------------------------------------------------------------
  1 | "CustomerID,Gender,Age,Annual Income (k$),Spending Score (1-100)"
  2 | "1,Male,19,15,39"
  3 | "2,Male,21,15,81"
  4 | "3,Female,20,16,6"
  5 | "4,Female,23,16,77"
  6 | "5,Female,31,17,40"
  7 | "6,Female,22,17,76"
  8 | "7,Female,35,18,6"
  9 | "8,Female,23,18,94"
 10 | "9,Male,64,19,3"
 11 | "10,Female,30,19,72"
 12 | "11,Male,67,19,14"
 13 | "12,Female,35,19,99"
 14 | "13,Female,58,20,15"
 15 | "14,Female,24,20,77"
 16 | "15,Male,37,20,13"
 17 | "16,Male,22,20,79"
 18 | "17,Female,35,21,35"
 19 | "18,Male,20,21,66"
 20 | "19,Male,52,23,29"
 21 | "20,Female,35,23,98"
 22 | "21,Male,35,24,35"
 23 | "22,Male,25,24,73"
 24 | "23,Female,46,25,5"
 25 | "24,Male,31,25,73"
 26 | "25,Female,54,28,14"
 27 | "26,Male,29,28,82"
 28 | "27,Female,45,28,32"
 29 | "28,Male,35,28,61"
 30 | "29,Female,40,29,31"
 31 | "30,Female,23,29,87"
 32 | "31,Male,60,30,4"
 33 | "32,Female,21,30,73"
 34 | "33,Male,53,33,4"
 35 | "34,Male,18,33,92"
 36 | "35,Female,49,33,14"
 37 | "36,Female,21,33,81"
 38 | "37,Female,42,34,17"
 39 | "38,Female,30,34,73"
 40 | "39,Female,36,37,26"
 41 | "40,Female,20,37,75"
 42 | "41,Female,65,38,35"
 43 | "42,Male,24,38,92"
 44 | "43,Male,48,39,36"
 45 | "44,Female,31,39,61"
 46 | "45,Female,49,39,28"
 47 | "46,Female,24,39,65"
 48 | "47,Female,50,40,55"
 49 | "48,Female,27,40,47"
 50 | "49,Female,29,40,42"
 51 | "50,Female,31,40,42"
 52 | "51,Female,49,42,52"
 53 | "52,Male,33,42,60"
 54 | "53,Female,31,43,54"
 55 | "54,Male,59,43,60"
 56 | "55,Female,50,43,45"
 57 | "56,Male,47,43,41"
 58 | "57,Female,51,44,50"
 59 | "58,Male,69,44,46"
 60 | "59,Female,27,46,51"
 61 | "60,Male,53,46,46"
 62 | "61,Male,70,46,56"
 63 | "62,Male,19,46,55"
 64 | "63,Female,67,47,52"
 65 | "64,Female,54,47,59"
 66 | "65,Male,63,48,51"
 67 | "66,Male,18,48,59"
 68 | "67,Female,43,48,50"
 69 | "68,Female,68,48,48"
 70 | "69,Male,19,48,59"
 71 | "70,Female,32,48,47"
 72 | "71,Male,70,49,55"
 73 | "72,Female,47,49,42"
 74 | "73,Female,60,50,49"
 75 | "74,Female,60,50,56"
 76 | "75,Male,59,54,47"
 77 | "76,Male,26,54,54"
 78 | "77,Female,45,54,53"
 79 | "78,Male,40,54,48"
 80 | "79,Female,23,54,52"
 81 | "80,Female,49,54,42"
 82 | "81,Male,57,54,51"
 83 | "82,Male,38,54,55"
 84 | "83,Male,67,54,41"
 85 | "84,Female,46,54,44"
 86 | "85,Female,21,54,57"
 87 | "86,Male,48,54,46"
 88 | "87,Female,55,57,58"
 89 | "88,Female,22,57,55"
 90 | "89,Female,34,58,60"
 91 | "90,Female,50,58,46"
 92 | "91,Female,68,59,55"
 93 | "92,Male,18,59,41"
 94 | "93,Male,48,60,49"
 95 | "94,Female,40,60,40"
 96 | "95,Female,32,60,42"
 97 | "96,Male,24,60,52"
 98 | "97,Female,47,60,47"
 99 | "98,Female,27,60,50"
100 | "99,Male,48,61,42"
101 | "100,Male,20,61,49"
102 | "101,Female,23,62,41"
103 | "102,Female,49,62,48"
104 | "103,Male,67,62,59"
105 | "104,Male,26,62,55"
106 | "105,Male,49,62,56"
107 | "106,Female,21,62,42"
108 | "107,Female,66,63,50"
109 | "108,Male,54,63,46"
110 | "109,Male,68,63,43"
111 | "110,Male,66,63,48"
112 | "111,Male,65,63,52"
113 | "112,Female,19,63,54"
114 | "113,Female,38,64,42"
115 | "114,Male,19,64,46"
116 | "115,Female,18,65,48"
117 | "116,Female,19,65,50"
118 | "117,Female,63,65,43"
119 | "118,Female,49,65,59"
120 | "119,Female,51,67,43"
121 | "120,Female,50,67,57"
122 | "121,Male,27,67,56"
123 | "122,Female,38,67,40"
124 | "123,Female,40,69,58"
125 | "124,Male,39,69,91"
126 | "125,Female,23,70,29"
127 | "126,Female,31,70,77"
128 | "127,Male,43,71,35"
129 | "128,Male,40,71,95"
130 | "129,Male,59,71,11"
131 | "130,Male,38,71,75"
132 | "131,Male,47,71,9"
133 | "132,Male,39,71,75"
134 | "133,Female,25,72,34"
135 | "134,Female,31,72,71"
136 | "135,Male,20,73,5"
137 | "136,Female,29,73,88"
138 | "137,Female,44,73,7"
139 | "138,Male,32,73,73"
140 | "139,Male,19,74,10"
141 | "140,Female,35,74,72"
142 | "141,Female,57,75,5"
143 | "142,Male,32,75,93"
144 | "143,Female,28,76,40"
145 | "144,Female,32,76,87"
146 | "145,Male,25,77,12"
147 | "146,Male,28,77,97"
148 | "147,Male,48,77,36"
149 | "148,Female,32,77,74"
150 | "149,Female,34,78,22"
151 | "150,Male,34,78,90"
152 | "151,Male,43,78,17"
153 | "152,Male,39,78,88"
154 | "153,Female,44,78,20"
155 | "154,Female,38,78,76"
156 | "155,Female,47,78,16"
157 | "156,Female,27,78,89"
158 | "157,Male,37,78,1"
159 | "158,Female,30,78,78"
160 | "159,Male,34,78,1"
161 | "160,Female,30,78,73"
162 | "161,Female,56,79,35"
163 | "162,Female,29,79,83"
164 | "163,Male,19,81,5"
165 | "164,Female,31,81,93"
166 | "165,Male,50,85,26"
167 | "166,Female,36,85,75"
168 | "167,Male,42,86,20"
169 | "168,Female,33,86,95"
170 | "169,Female,36,87,27"
171 | "170,Male,32,87,63"
172 | "171,Male,40,87,13"
173 | "172,Male,28,87,75"
174 | "173,Male,36,87,10"
175 | "174,Male,36,87,92"
176 | "175,Female,52,88,13"
177 | "176,Female,30,88,86"
178 | "177,Male,58,88,15"
179 | "178,Male,27,88,69"
180 | "179,Male,59,93,14"
181 | "180,Male,35,93,90"
182 | "181,Female,37,97,32"
183 | "182,Female,32,97,86"
184 | "183,Male,46,98,15"
185 | "184,Female,29,98,88"
186 | "185,Female,41,99,39"
187 | "186,Male,30,99,97"
188 | "187,Female,54,101,24"
189 | "188,Male,28,101,68"
190 | "189,Female,41,103,17"
191 | "190,Female,36,103,85"
192 | "191,Female,34,103,23"
193 | "192,Female,32,103,69"
194 | "193,Male,33,113,8"
195 | "194,Female,38,113,91"
196 | "195,Female,47,120,16"
197 | "196,Female,35,120,79"
198 | "197,Female,45,126,28"
199 | "198,Male,32,126,74"
200 | "199,Male,32,137,18"
201 | "200,Male,30,137,83"
202 | 


--------------------------------------------------------------------------------
/mallCustomerData.txt:
--------------------------------------------------------------------------------
  1 | CustomerID,Gender,Age,Annual Income (k$),Spending Score (1-100)
  2 | 1,Male,19,15,39
  3 | 2,Male,21,15,81
  4 | 3,Female,20,16,6
  5 | 4,Female,23,16,77
  6 | 5,Female,31,17,40
  7 | 6,Female,22,17,76
  8 | 7,Female,35,18,6
  9 | 8,Female,23,18,94
 10 | 9,Male,64,19,3
 11 | 10,Female,30,19,72
 12 | 11,Male,67,19,14
 13 | 12,Female,35,19,99
 14 | 13,Female,58,20,15
 15 | 14,Female,24,20,77
 16 | 15,Male,37,20,13
 17 | 16,Male,22,20,79
 18 | 17,Female,35,21,35
 19 | 18,Male,20,21,66
 20 | 19,Male,52,23,29
 21 | 20,Female,35,23,98
 22 | 21,Male,35,24,35
 23 | 22,Male,25,24,73
 24 | 23,Female,46,25,5
 25 | 24,Male,31,25,73
 26 | 25,Female,54,28,14
 27 | 26,Male,29,28,82
 28 | 27,Female,45,28,32
 29 | 28,Male,35,28,61
 30 | 29,Female,40,29,31
 31 | 30,Female,23,29,87
 32 | 31,Male,60,30,4
 33 | 32,Female,21,30,73
 34 | 33,Male,53,33,4
 35 | 34,Male,18,33,92
 36 | 35,Female,49,33,14
 37 | 36,Female,21,33,81
 38 | 37,Female,42,34,17
 39 | 38,Female,30,34,73
 40 | 39,Female,36,37,26
 41 | 40,Female,20,37,75
 42 | 41,Female,65,38,35
 43 | 42,Male,24,38,92
 44 | 43,Male,48,39,36
 45 | 44,Female,31,39,61
 46 | 45,Female,49,39,28
 47 | 46,Female,24,39,65
 48 | 47,Female,50,40,55
 49 | 48,Female,27,40,47
 50 | 49,Female,29,40,42
 51 | 50,Female,31,40,42
 52 | 51,Female,49,42,52
 53 | 52,Male,33,42,60
 54 | 53,Female,31,43,54
 55 | 54,Male,59,43,60
 56 | 55,Female,50,43,45
 57 | 56,Male,47,43,41
 58 | 57,Female,51,44,50
 59 | 58,Male,69,44,46
 60 | 59,Female,27,46,51
 61 | 60,Male,53,46,46
 62 | 61,Male,70,46,56
 63 | 62,Male,19,46,55
 64 | 63,Female,67,47,52
 65 | 64,Female,54,47,59
 66 | 65,Male,63,48,51
 67 | 66,Male,18,48,59
 68 | 67,Female,43,48,50
 69 | 68,Female,68,48,48
 70 | 69,Male,19,48,59
 71 | 70,Female,32,48,47
 72 | 71,Male,70,49,55
 73 | 72,Female,47,49,42
 74 | 73,Female,60,50,49
 75 | 74,Female,60,50,56
 76 | 75,Male,59,54,47
 77 | 76,Male,26,54,54
 78 | 77,Female,45,54,53
 79 | 78,Male,40,54,48
 80 | 79,Female,23,54,52
 81 | 80,Female,49,54,42
 82 | 81,Male,57,54,51
 83 | 82,Male,38,54,55
 84 | 83,Male,67,54,41
 85 | 84,Female,46,54,44
 86 | 85,Female,21,54,57
 87 | 86,Male,48,54,46
 88 | 87,Female,55,57,58
 89 | 88,Female,22,57,55
 90 | 89,Female,34,58,60
 91 | 90,Female,50,58,46
 92 | 91,Female,68,59,55
 93 | 92,Male,18,59,41
 94 | 93,Male,48,60,49
 95 | 94,Female,40,60,40
 96 | 95,Female,32,60,42
 97 | 96,Male,24,60,52
 98 | 97,Female,47,60,47
 99 | 98,Female,27,60,50
100 | 99,Male,48,61,42
101 | 100,Male,20,61,49
102 | 101,Female,23,62,41
103 | 102,Female,49,62,48
104 | 103,Male,67,62,59
105 | 104,Male,26,62,55
106 | 105,Male,49,62,56
107 | 106,Female,21,62,42
108 | 107,Female,66,63,50
109 | 108,Male,54,63,46
110 | 109,Male,68,63,43
111 | 110,Male,66,63,48
112 | 111,Male,65,63,52
113 | 112,Female,19,63,54
114 | 113,Female,38,64,42
115 | 114,Male,19,64,46
116 | 115,Female,18,65,48
117 | 116,Female,19,65,50
118 | 117,Female,63,65,43
119 | 118,Female,49,65,59
120 | 119,Female,51,67,43
121 | 120,Female,50,67,57
122 | 121,Male,27,67,56
123 | 122,Female,38,67,40
124 | 123,Female,40,69,58
125 | 124,Male,39,69,91
126 | 125,Female,23,70,29
127 | 126,Female,31,70,77
128 | 127,Male,43,71,35
129 | 128,Male,40,71,95
130 | 129,Male,59,71,11
131 | 130,Male,38,71,75
132 | 131,Male,47,71,9
133 | 132,Male,39,71,75
134 | 133,Female,25,72,34
135 | 134,Female,31,72,71
136 | 135,Male,20,73,5
137 | 136,Female,29,73,88
138 | 137,Female,44,73,7
139 | 138,Male,32,73,73
140 | 139,Male,19,74,10
141 | 140,Female,35,74,72
142 | 141,Female,57,75,5
143 | 142,Male,32,75,93
144 | 143,Female,28,76,40
145 | 144,Female,32,76,87
146 | 145,Male,25,77,12
147 | 146,Male,28,77,97
148 | 147,Male,48,77,36
149 | 148,Female,32,77,74
150 | 149,Female,34,78,22
151 | 150,Male,34,78,90
152 | 151,Male,43,78,17
153 | 152,Male,39,78,88
154 | 153,Female,44,78,20
155 | 154,Female,38,78,76
156 | 155,Female,47,78,16
157 | 156,Female,27,78,89
158 | 157,Male,37,78,1
159 | 158,Female,30,78,78
160 | 159,Male,34,78,1
161 | 160,Female,30,78,73
162 | 161,Female,56,79,35
163 | 162,Female,29,79,83
164 | 163,Male,19,81,5
165 | 164,Female,31,81,93
166 | 165,Male,50,85,26
167 | 166,Female,36,85,75
168 | 167,Male,42,86,20
169 | 168,Female,33,86,95
170 | 169,Female,36,87,27
171 | 170,Male,32,87,63
172 | 171,Male,40,87,13
173 | 172,Male,28,87,75
174 | 173,Male,36,87,10
175 | 174,Male,36,87,92
176 | 175,Female,52,88,13
177 | 176,Female,30,88,86
178 | 177,Male,58,88,15
179 | 178,Male,27,88,69
180 | 179,Male,59,93,14
181 | 180,Male,35,93,90
182 | 181,Female,37,97,32
183 | 182,Female,32,97,86
184 | 183,Male,46,98,15
185 | 184,Female,29,98,88
186 | 185,Female,41,99,39
187 | 186,Male,30,99,97
188 | 187,Female,54,101,24
189 | 188,Male,28,101,68
190 | 189,Female,41,103,17
191 | 190,Female,36,103,85
192 | 191,Female,34,103,23
193 | 192,Female,32,103,69
194 | 193,Male,33,113,8
195 | 194,Female,38,113,91
196 | 195,Female,47,120,16
197 | 196,Female,35,120,79
198 | 197,Female,45,126,28
199 | 198,Male,32,126,74
200 | 199,Male,32,137,18
201 | 200,Male,30,137,83
202 | 


--------------------------------------------------------------------------------
/salaries.csv:
--------------------------------------------------------------------------------
 1 | company,job,degree,salary_more_then_100k
 2 | google,sale exective,bachelaors,0
 3 | google,sale exective,masters,0
 4 | google,business manager,bachelaors,1
 5 | google,business manager,masters,1
 6 | google,computer programmer,bachelaors,0
 7 | google,computer programmer,masters,1
 8 | abc pharma,sale exective,masters,0
 9 | abc pharma,computer programmer,bachelaors,0
10 | abc pharma,business manager,bachelaors,0
11 | abc pharma,business manager,masters,1
12 | facebook,sale exective,bachelaors,1
13 | facebook,sale exective,masters,1
14 | facebook,business manager,bachelaors,1
15 | facebook,business manager,masters,1
16 | facebook,computer programmer,bachelaors,1
17 | facebook,computer programmer,masters,1
18 | 


--------------------------------------------------------------------------------
/user+data.csv:
--------------------------------------------------------------------------------
  1 | User ID,Gender,Age,EstimatedSalary,Purchased
  2 | 15624510,Male,19,19000,0
  3 | 15810944,Male,35,20000,0
  4 | 15668575,Female,26,43000,0
  5 | 15603246,Female,27,57000,0
  6 | 15804002,Male,19,76000,0
  7 | 15728773,Male,27,58000,0
  8 | 15598044,Female,27,84000,0
  9 | 15694829,Female,32,150000,1
 10 | 15600575,Male,25,33000,0
 11 | 15727311,Female,35,65000,0
 12 | 15570769,Female,26,80000,0
 13 | 15606274,Female,26,52000,0
 14 | 15746139,Male,20,86000,0
 15 | 15704987,Male,32,18000,0
 16 | 15628972,Male,18,82000,0
 17 | 15697686,Male,29,80000,0
 18 | 15733883,Male,47,25000,1
 19 | 15617482,Male,45,26000,1
 20 | 15704583,Male,46,28000,1
 21 | 15621083,Female,48,29000,1
 22 | 15649487,Male,45,22000,1
 23 | 15736760,Female,47,49000,1
 24 | 15714658,Male,48,41000,1
 25 | 15599081,Female,45,22000,1
 26 | 15705113,Male,46,23000,1
 27 | 15631159,Male,47,20000,1
 28 | 15792818,Male,49,28000,1
 29 | 15633531,Female,47,30000,1
 30 | 15744529,Male,29,43000,0
 31 | 15669656,Male,31,18000,0
 32 | 15581198,Male,31,74000,0
 33 | 15729054,Female,27,137000,1
 34 | 15573452,Female,21,16000,0
 35 | 15776733,Female,28,44000,0
 36 | 15724858,Male,27,90000,0
 37 | 15713144,Male,35,27000,0
 38 | 15690188,Female,33,28000,0
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 41 | 15782806,Female,27,31000,0
 42 | 15764419,Female,27,17000,0
 43 | 15591915,Female,33,51000,0
 44 | 15772798,Male,35,108000,0
 45 | 15792008,Male,30,15000,0
 46 | 15715541,Female,28,84000,0
 47 | 15639277,Male,23,20000,0
 48 | 15798850,Male,25,79000,0
 49 | 15776348,Female,27,54000,0
 50 | 15727696,Male,30,135000,1
 51 | 15793813,Female,31,89000,0
 52 | 15694395,Female,24,32000,0
 53 | 15764195,Female,18,44000,0
 54 | 15744919,Female,29,83000,0
 55 | 15671655,Female,35,23000,0
 56 | 15654901,Female,27,58000,0
 57 | 15649136,Female,24,55000,0
 58 | 15775562,Female,23,48000,0
 59 | 15807481,Male,28,79000,0
 60 | 15642885,Male,22,18000,0
 61 | 15789109,Female,32,117000,0
 62 | 15814004,Male,27,20000,0
 63 | 15673619,Male,25,87000,0
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 84 | 15709476,Male,20,49000,0
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 86 | 15798659,Female,30,62000,0
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 88 | 15694946,Male,24,55000,0
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 90 | 15768816,Male,26,81000,0
 91 | 15682268,Male,35,50000,0
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 93 | 15636428,Female,30,116000,0
 94 | 15809823,Male,26,15000,0
 95 | 15699284,Female,29,28000,0
 96 | 15786993,Female,29,83000,0
 97 | 15709441,Female,35,44000,0
 98 | 15710257,Female,35,25000,0
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100 | 15575694,Male,35,73000,0
101 | 15756820,Female,28,37000,0
102 | 15766289,Male,27,88000,0
103 | 15593014,Male,28,59000,0
104 | 15584545,Female,32,86000,0
105 | 15675949,Female,33,149000,1
106 | 15672091,Female,19,21000,0
107 | 15801658,Male,21,72000,0
108 | 15706185,Female,26,35000,0
109 | 15789863,Male,27,89000,0
110 | 15720943,Male,26,86000,0
111 | 15697997,Female,38,80000,0
112 | 15665416,Female,39,71000,0
113 | 15660200,Female,37,71000,0
114 | 15619653,Male,38,61000,0
115 | 15773447,Male,37,55000,0
116 | 15739160,Male,42,80000,0
117 | 15689237,Male,40,57000,0
118 | 15679297,Male,35,75000,0
119 | 15591433,Male,36,52000,0
120 | 15642725,Male,40,59000,0
121 | 15701962,Male,41,59000,0
122 | 15811613,Female,36,75000,0
123 | 15741049,Male,37,72000,0
124 | 15724423,Female,40,75000,0
125 | 15574305,Male,35,53000,0
126 | 15678168,Female,41,51000,0
127 | 15697020,Female,39,61000,0
128 | 15610801,Male,42,65000,0
129 | 15745232,Male,26,32000,0
130 | 15722758,Male,30,17000,0
131 | 15792102,Female,26,84000,0
132 | 15675185,Male,31,58000,0
133 | 15801247,Male,33,31000,0
134 | 15725660,Male,30,87000,0
135 | 15638963,Female,21,68000,0
136 | 15800061,Female,28,55000,0
137 | 15578006,Male,23,63000,0
138 | 15668504,Female,20,82000,0
139 | 15687491,Male,30,107000,1
140 | 15610403,Female,28,59000,0
141 | 15741094,Male,19,25000,0
142 | 15807909,Male,19,85000,0
143 | 15666141,Female,18,68000,0
144 | 15617134,Male,35,59000,0
145 | 15783029,Male,30,89000,0
146 | 15622833,Female,34,25000,0
147 | 15746422,Female,24,89000,0
148 | 15750839,Female,27,96000,1
149 | 15749130,Female,41,30000,0
150 | 15779862,Male,29,61000,0
151 | 15767871,Male,20,74000,0
152 | 15679651,Female,26,15000,0
153 | 15576219,Male,41,45000,0
154 | 15699247,Male,31,76000,0
155 | 15619087,Female,36,50000,0
156 | 15605327,Male,40,47000,0
157 | 15610140,Female,31,15000,0
158 | 15791174,Male,46,59000,0
159 | 15602373,Male,29,75000,0
160 | 15762605,Male,26,30000,0
161 | 15598840,Female,32,135000,1
162 | 15744279,Male,32,100000,1
163 | 15670619,Male,25,90000,0
164 | 15599533,Female,37,33000,0
165 | 15757837,Male,35,38000,0
166 | 15697574,Female,33,69000,0
167 | 15578738,Female,18,86000,0
168 | 15762228,Female,22,55000,0
169 | 15614827,Female,35,71000,0
170 | 15789815,Male,29,148000,1
171 | 15579781,Female,29,47000,0
172 | 15587013,Male,21,88000,0
173 | 15570932,Male,34,115000,0
174 | 15794661,Female,26,118000,0
175 | 15581654,Female,34,43000,0
176 | 15644296,Female,34,72000,0
177 | 15614420,Female,23,28000,0
178 | 15609653,Female,35,47000,0
179 | 15594577,Male,25,22000,0
180 | 15584114,Male,24,23000,0
181 | 15673367,Female,31,34000,0
182 | 15685576,Male,26,16000,0
183 | 15774727,Female,31,71000,0
184 | 15694288,Female,32,117000,1
185 | 15603319,Male,33,43000,0
186 | 15759066,Female,33,60000,0
187 | 15814816,Male,31,66000,0
188 | 15724402,Female,20,82000,0
189 | 15571059,Female,33,41000,0
190 | 15674206,Male,35,72000,0
191 | 15715160,Male,28,32000,0
192 | 15730448,Male,24,84000,0
193 | 15662067,Female,19,26000,0
194 | 15779581,Male,29,43000,0
195 | 15662901,Male,19,70000,0
196 | 15689751,Male,28,89000,0
197 | 15667742,Male,34,43000,0
198 | 15738448,Female,30,79000,0
199 | 15680243,Female,20,36000,0
200 | 15745083,Male,26,80000,0
201 | 15708228,Male,35,22000,0
202 | 15628523,Male,35,39000,0
203 | 15708196,Male,49,74000,0
204 | 15735549,Female,39,134000,1
205 | 15809347,Female,41,71000,0
206 | 15660866,Female,58,101000,1
207 | 15766609,Female,47,47000,0
208 | 15654230,Female,55,130000,1
209 | 15794566,Female,52,114000,0
210 | 15800890,Female,40,142000,1
211 | 15697424,Female,46,22000,0
212 | 15724536,Female,48,96000,1
213 | 15735878,Male,52,150000,1
214 | 15707596,Female,59,42000,0
215 | 15657163,Male,35,58000,0
216 | 15622478,Male,47,43000,0
217 | 15779529,Female,60,108000,1
218 | 15636023,Male,49,65000,0
219 | 15582066,Male,40,78000,0
220 | 15666675,Female,46,96000,0
221 | 15732987,Male,59,143000,1
222 | 15789432,Female,41,80000,0
223 | 15663161,Male,35,91000,1
224 | 15694879,Male,37,144000,1
225 | 15593715,Male,60,102000,1
226 | 15575002,Female,35,60000,0
227 | 15622171,Male,37,53000,0
228 | 15795224,Female,36,126000,1
229 | 15685346,Male,56,133000,1
230 | 15691808,Female,40,72000,0
231 | 15721007,Female,42,80000,1
232 | 15794253,Female,35,147000,1
233 | 15694453,Male,39,42000,0
234 | 15813113,Male,40,107000,1
235 | 15614187,Male,49,86000,1
236 | 15619407,Female,38,112000,0
237 | 15646227,Male,46,79000,1
238 | 15660541,Male,40,57000,0
239 | 15753874,Female,37,80000,0
240 | 15617877,Female,46,82000,0
241 | 15772073,Female,53,143000,1
242 | 15701537,Male,42,149000,1
243 | 15736228,Male,38,59000,0
244 | 15780572,Female,50,88000,1
245 | 15769596,Female,56,104000,1
246 | 15586996,Female,41,72000,0
247 | 15722061,Female,51,146000,1
248 | 15638003,Female,35,50000,0
249 | 15775590,Female,57,122000,1
250 | 15730688,Male,41,52000,0
251 | 15753102,Female,35,97000,1
252 | 15810075,Female,44,39000,0
253 | 15723373,Male,37,52000,0
254 | 15795298,Female,48,134000,1
255 | 15584320,Female,37,146000,1
256 | 15724161,Female,50,44000,0
257 | 15750056,Female,52,90000,1
258 | 15609637,Female,41,72000,0
259 | 15794493,Male,40,57000,0
260 | 15569641,Female,58,95000,1
261 | 15815236,Female,45,131000,1
262 | 15811177,Female,35,77000,0
263 | 15680587,Male,36,144000,1
264 | 15672821,Female,55,125000,1
265 | 15767681,Female,35,72000,0
266 | 15600379,Male,48,90000,1
267 | 15801336,Female,42,108000,1
268 | 15721592,Male,40,75000,0
269 | 15581282,Male,37,74000,0
270 | 15746203,Female,47,144000,1
271 | 15583137,Male,40,61000,0
272 | 15680752,Female,43,133000,0
273 | 15688172,Female,59,76000,1
274 | 15791373,Male,60,42000,1
275 | 15589449,Male,39,106000,1
276 | 15692819,Female,57,26000,1
277 | 15727467,Male,57,74000,1
278 | 15734312,Male,38,71000,0
279 | 15764604,Male,49,88000,1
280 | 15613014,Female,52,38000,1
281 | 15759684,Female,50,36000,1
282 | 15609669,Female,59,88000,1
283 | 15685536,Male,35,61000,0
284 | 15750447,Male,37,70000,1
285 | 15663249,Female,52,21000,1
286 | 15638646,Male,48,141000,0
287 | 15734161,Female,37,93000,1
288 | 15631070,Female,37,62000,0
289 | 15761950,Female,48,138000,1
290 | 15649668,Male,41,79000,0
291 | 15713912,Female,37,78000,1
292 | 15586757,Male,39,134000,1
293 | 15596522,Male,49,89000,1
294 | 15625395,Male,55,39000,1
295 | 15760570,Male,37,77000,0
296 | 15566689,Female,35,57000,0
297 | 15725794,Female,36,63000,0
298 | 15673539,Male,42,73000,1
299 | 15705298,Female,43,112000,1
300 | 15675791,Male,45,79000,0
301 | 15747043,Male,46,117000,1
302 | 15736397,Female,58,38000,1
303 | 15678201,Male,48,74000,1
304 | 15720745,Female,37,137000,1
305 | 15637593,Male,37,79000,1
306 | 15598070,Female,40,60000,0
307 | 15787550,Male,42,54000,0
308 | 15603942,Female,51,134000,0
309 | 15733973,Female,47,113000,1
310 | 15596761,Male,36,125000,1
311 | 15652400,Female,38,50000,0
312 | 15717893,Female,42,70000,0
313 | 15622585,Male,39,96000,1
314 | 15733964,Female,38,50000,0
315 | 15753861,Female,49,141000,1
316 | 15747097,Female,39,79000,0
317 | 15594762,Female,39,75000,1
318 | 15667417,Female,54,104000,1
319 | 15684861,Male,35,55000,0
320 | 15742204,Male,45,32000,1
321 | 15623502,Male,36,60000,0
322 | 15774872,Female,52,138000,1
323 | 15611191,Female,53,82000,1
324 | 15674331,Male,41,52000,0
325 | 15619465,Female,48,30000,1
326 | 15575247,Female,48,131000,1
327 | 15695679,Female,41,60000,0
328 | 15713463,Male,41,72000,0
329 | 15785170,Female,42,75000,0
330 | 15796351,Male,36,118000,1
331 | 15639576,Female,47,107000,1
332 | 15693264,Male,38,51000,0
333 | 15589715,Female,48,119000,1
334 | 15769902,Male,42,65000,0
335 | 15587177,Male,40,65000,0
336 | 15814553,Male,57,60000,1
337 | 15601550,Female,36,54000,0
338 | 15664907,Male,58,144000,1
339 | 15612465,Male,35,79000,0
340 | 15810800,Female,38,55000,0
341 | 15665760,Male,39,122000,1
342 | 15588080,Female,53,104000,1
343 | 15776844,Male,35,75000,0
344 | 15717560,Female,38,65000,0
345 | 15629739,Female,47,51000,1
346 | 15729908,Male,47,105000,1
347 | 15716781,Female,41,63000,0
348 | 15646936,Male,53,72000,1
349 | 15768151,Female,54,108000,1
350 | 15579212,Male,39,77000,0
351 | 15721835,Male,38,61000,0
352 | 15800515,Female,38,113000,1
353 | 15591279,Male,37,75000,0
354 | 15587419,Female,42,90000,1
355 | 15750335,Female,37,57000,0
356 | 15699619,Male,36,99000,1
357 | 15606472,Male,60,34000,1
358 | 15778368,Male,54,70000,1
359 | 15671387,Female,41,72000,0
360 | 15573926,Male,40,71000,1
361 | 15709183,Male,42,54000,0
362 | 15577514,Male,43,129000,1
363 | 15778830,Female,53,34000,1
364 | 15768072,Female,47,50000,1
365 | 15768293,Female,42,79000,0
366 | 15654456,Male,42,104000,1
367 | 15807525,Female,59,29000,1
368 | 15574372,Female,58,47000,1
369 | 15671249,Male,46,88000,1
370 | 15779744,Male,38,71000,0
371 | 15624755,Female,54,26000,1
372 | 15611430,Female,60,46000,1
373 | 15774744,Male,60,83000,1
374 | 15629885,Female,39,73000,0
375 | 15708791,Male,59,130000,1
376 | 15793890,Female,37,80000,0
377 | 15646091,Female,46,32000,1
378 | 15596984,Female,46,74000,0
379 | 15800215,Female,42,53000,0
380 | 15577806,Male,41,87000,1
381 | 15749381,Female,58,23000,1
382 | 15683758,Male,42,64000,0
383 | 15670615,Male,48,33000,1
384 | 15715622,Female,44,139000,1
385 | 15707634,Male,49,28000,1
386 | 15806901,Female,57,33000,1
387 | 15775335,Male,56,60000,1
388 | 15724150,Female,49,39000,1
389 | 15627220,Male,39,71000,0
390 | 15672330,Male,47,34000,1
391 | 15668521,Female,48,35000,1
392 | 15807837,Male,48,33000,1
393 | 15592570,Male,47,23000,1
394 | 15748589,Female,45,45000,1
395 | 15635893,Male,60,42000,1
396 | 15757632,Female,39,59000,0
397 | 15691863,Female,46,41000,1
398 | 15706071,Male,51,23000,1
399 | 15654296,Female,50,20000,1
400 | 15755018,Male,36,33000,0
401 | 15594041,Female,49,36000,1


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