├── README.md
├── model.h5
├── scaler.pkl
├── onehot_encoder_geo.pkl
├── label_encoder_gender.pkl
├── requirements.txt
├── app.py
├── prediction.ipynb
├── LICENSE
└── experiments.ipynb


/README.md:
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1 | # ANN-CLassification-Churn


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/model.h5:
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https://raw.githubusercontent.com/krishnaik06/ANN-CLassification-Churn/HEAD/model.h5


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/scaler.pkl:
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https://raw.githubusercontent.com/krishnaik06/ANN-CLassification-Churn/HEAD/scaler.pkl


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/onehot_encoder_geo.pkl:
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https://raw.githubusercontent.com/krishnaik06/ANN-CLassification-Churn/HEAD/onehot_encoder_geo.pkl


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/label_encoder_gender.pkl:
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https://raw.githubusercontent.com/krishnaik06/ANN-CLassification-Churn/HEAD/label_encoder_gender.pkl


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/requirements.txt:
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1 | tensorflow==2.15.0
2 | pandas 
3 | numpy 
4 | scikit-learn
5 | tensorboard
6 | matplotlib
7 | streamlit


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/app.py:
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 1 | import streamlit as st
 2 | import numpy as np
 3 | import tensorflow as tf
 4 | from sklearn.preprocessing import StandardScaler, LabelEncoder, OneHotEncoder
 5 | import pandas as pd
 6 | import pickle
 7 | 
 8 | # Load the trained model
 9 | model = tf.keras.models.load_model('model.h5')
10 | 
11 | # Load the encoders and scaler
12 | with open('label_encoder_gender.pkl', 'rb') as file:
13 |     label_encoder_gender = pickle.load(file)
14 | 
15 | with open('onehot_encoder_geo.pkl', 'rb') as file:
16 |     onehot_encoder_geo = pickle.load(file)
17 | 
18 | with open('scaler.pkl', 'rb') as file:
19 |     scaler = pickle.load(file)
20 | 
21 | 
22 | ## streamlit app
23 | st.title('Customer Churn PRediction')
24 | 
25 | # User input
26 | geography = st.selectbox('Geography', onehot_encoder_geo.categories_[0])
27 | gender = st.selectbox('Gender', label_encoder_gender.classes_)
28 | age = st.slider('Age', 18, 92)
29 | balance = st.number_input('Balance')
30 | credit_score = st.number_input('Credit Score')
31 | estimated_salary = st.number_input('Estimated Salary')
32 | tenure = st.slider('Tenure', 0, 10)
33 | num_of_products = st.slider('Number of Products', 1, 4)
34 | has_cr_card = st.selectbox('Has Credit Card', [0, 1])
35 | is_active_member = st.selectbox('Is Active Member', [0, 1])
36 | 
37 | # Prepare the input data
38 | input_data = pd.DataFrame({
39 |     'CreditScore': [credit_score],
40 |     'Gender': [label_encoder_gender.transform([gender])[0]],
41 |     'Age': [age],
42 |     'Tenure': [tenure],
43 |     'Balance': [balance],
44 |     'NumOfProducts': [num_of_products],
45 |     'HasCrCard': [has_cr_card],
46 |     'IsActiveMember': [is_active_member],
47 |     'EstimatedSalary': [estimated_salary]
48 | })
49 | 
50 | # One-hot encode 'Geography'
51 | geo_encoded = onehot_encoder_geo.transform([[geography]]).toarray()
52 | geo_encoded_df = pd.DataFrame(geo_encoded, columns=onehot_encoder_geo.get_feature_names_out(['Geography']))
53 | 
54 | # Combine one-hot encoded columns with input data
55 | input_data = pd.concat([input_data.reset_index(drop=True), geo_encoded_df], axis=1)
56 | 
57 | # Scale the input data
58 | input_data_scaled = scaler.transform(input_data)
59 | 
60 | 
61 | # Predict churn
62 | prediction = model.predict(input_data_scaled)
63 | prediction_proba = prediction[0][0]
64 | 
65 | st.write(f'Churn Probability: {prediction_proba:.2f}')
66 | 
67 | if prediction_proba > 0.5:
68 |     st.write('The customer is likely to churn.')
69 | else:
70 |     st.write('The customer is not likely to churn.')
71 | 


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/prediction.ipynb:
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  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "code",
  5 |    "execution_count": 1,
  6 |    "metadata": {},
  7 |    "outputs": [
  8 |     {
  9 |      "name": "stdout",
 10 |      "output_type": "stream",
 11 |      "text": [
 12 |       "WARNING:tensorflow:From e:\\UDemy Final\\ANN Classification\\venv\\Lib\\site-packages\\keras\\src\\losses.py:2976: The name tf.losses.sparse_softmax_cross_entropy is deprecated. Please use tf.compat.v1.losses.sparse_softmax_cross_entropy instead.\n",
 13 |       "\n"
 14 |      ]
 15 |     }
 16 |    ],
 17 |    "source": [
 18 |     "import tensorflow as tf\n",
 19 |     "from tensorflow.keras.models import load_model\n",
 20 |     "import pickle\n",
 21 |     "import pandas as pd\n",
 22 |     "import numpy as np"
 23 |    ]
 24 |   },
 25 |   {
 26 |    "cell_type": "code",
 27 |    "execution_count": 3,
 28 |    "metadata": {},
 29 |    "outputs": [],
 30 |    "source": [
 31 |     "### Load the trained model, scaler pickle,onehot\n",
 32 |     "model=load_model('model.h5')\n",
 33 |     "\n",
 34 |     "## load the encoder and scaler\n",
 35 |     "with open('onehot_encoder_geo.pkl','rb') as file:\n",
 36 |     "    label_encoder_geo=pickle.load(file)\n",
 37 |     "\n",
 38 |     "with open('label_encoder_gender.pkl', 'rb') as file:\n",
 39 |     "    label_encoder_gender = pickle.load(file)\n",
 40 |     "\n",
 41 |     "with open('scaler.pkl', 'rb') as file:\n",
 42 |     "    scaler = pickle.load(file)"
 43 |    ]
 44 |   },
 45 |   {
 46 |    "cell_type": "code",
 47 |    "execution_count": 4,
 48 |    "metadata": {},
 49 |    "outputs": [],
 50 |    "source": [
 51 |     "# Example input data\n",
 52 |     "input_data = {\n",
 53 |     "    'CreditScore': 600,\n",
 54 |     "    'Geography': 'France',\n",
 55 |     "    'Gender': 'Male',\n",
 56 |     "    'Age': 40,\n",
 57 |     "    'Tenure': 3,\n",
 58 |     "    'Balance': 60000,\n",
 59 |     "    'NumOfProducts': 2,\n",
 60 |     "    'HasCrCard': 1,\n",
 61 |     "    'IsActiveMember': 1,\n",
 62 |     "    'EstimatedSalary': 50000\n",
 63 |     "}"
 64 |    ]
 65 |   },
 66 |   {
 67 |    "cell_type": "code",
 68 |    "execution_count": 7,
 69 |    "metadata": {},
 70 |    "outputs": [
 71 |     {
 72 |      "name": "stderr",
 73 |      "output_type": "stream",
 74 |      "text": [
 75 |       "e:\\UDemy Final\\ANN Classification\\venv\\Lib\\site-packages\\sklearn\\base.py:493: UserWarning: X does not have valid feature names, but OneHotEncoder was fitted with feature names\n",
 76 |       "  warnings.warn(\n"
 77 |      ]
 78 |     },
 79 |     {
 80 |      "data": {
 81 |       "text/html": [
 82 |        "<div>\n",
 83 |        "<style scoped>\n",
 84 |        "    .dataframe tbody tr th:only-of-type {\n",
 85 |        "        vertical-align: middle;\n",
 86 |        "    }\n",
 87 |        "\n",
 88 |        "    .dataframe tbody tr th {\n",
 89 |        "        vertical-align: top;\n",
 90 |        "    }\n",
 91 |        "\n",
 92 |        "    .dataframe thead th {\n",
 93 |        "        text-align: right;\n",
 94 |        "    }\n",
 95 |        "</style>\n",
 96 |        "<table border=\"1\" class=\"dataframe\">\n",
 97 |        "  <thead>\n",
 98 |        "    <tr style=\"text-align: right;\">\n",
 99 |        "      <th></th>\n",
100 |        "      <th>Geography_France</th>\n",
101 |        "      <th>Geography_Germany</th>\n",
102 |        "      <th>Geography_Spain</th>\n",
103 |        "    </tr>\n",
104 |        "  </thead>\n",
105 |        "  <tbody>\n",
106 |        "    <tr>\n",
107 |        "      <th>0</th>\n",
108 |        "      <td>1.0</td>\n",
109 |        "      <td>0.0</td>\n",
110 |        "      <td>0.0</td>\n",
111 |        "    </tr>\n",
112 |        "  </tbody>\n",
113 |        "</table>\n",
114 |        "</div>"
115 |       ],
116 |       "text/plain": [
117 |        "   Geography_France  Geography_Germany  Geography_Spain\n",
118 |        "0               1.0                0.0              0.0"
119 |       ]
120 |      },
121 |      "execution_count": 7,
122 |      "metadata": {},
123 |      "output_type": "execute_result"
124 |     }
125 |    ],
126 |    "source": [
127 |     "# One-hot encode 'Geography'\n",
128 |     "geo_encoded = label_encoder_geo.transform([[input_data['Geography']]]).toarray()\n",
129 |     "geo_encoded_df = pd.DataFrame(geo_encoded, columns=label_encoder_geo.get_feature_names_out(['Geography']))\n",
130 |     "geo_encoded_df\n"
131 |    ]
132 |   },
133 |   {
134 |    "cell_type": "code",
135 |    "execution_count": 9,
136 |    "metadata": {},
137 |    "outputs": [
138 |     {
139 |      "data": {
140 |       "text/html": [
141 |        "<div>\n",
142 |        "<style scoped>\n",
143 |        "    .dataframe tbody tr th:only-of-type {\n",
144 |        "        vertical-align: middle;\n",
145 |        "    }\n",
146 |        "\n",
147 |        "    .dataframe tbody tr th {\n",
148 |        "        vertical-align: top;\n",
149 |        "    }\n",
150 |        "\n",
151 |        "    .dataframe thead th {\n",
152 |        "        text-align: right;\n",
153 |        "    }\n",
154 |        "</style>\n",
155 |        "<table border=\"1\" class=\"dataframe\">\n",
156 |        "  <thead>\n",
157 |        "    <tr style=\"text-align: right;\">\n",
158 |        "      <th></th>\n",
159 |        "      <th>CreditScore</th>\n",
160 |        "      <th>Geography</th>\n",
161 |        "      <th>Gender</th>\n",
162 |        "      <th>Age</th>\n",
163 |        "      <th>Tenure</th>\n",
164 |        "      <th>Balance</th>\n",
165 |        "      <th>NumOfProducts</th>\n",
166 |        "      <th>HasCrCard</th>\n",
167 |        "      <th>IsActiveMember</th>\n",
168 |        "      <th>EstimatedSalary</th>\n",
169 |        "    </tr>\n",
170 |        "  </thead>\n",
171 |        "  <tbody>\n",
172 |        "    <tr>\n",
173 |        "      <th>0</th>\n",
174 |        "      <td>600</td>\n",
175 |        "      <td>France</td>\n",
176 |        "      <td>Male</td>\n",
177 |        "      <td>40</td>\n",
178 |        "      <td>3</td>\n",
179 |        "      <td>60000</td>\n",
180 |        "      <td>2</td>\n",
181 |        "      <td>1</td>\n",
182 |        "      <td>1</td>\n",
183 |        "      <td>50000</td>\n",
184 |        "    </tr>\n",
185 |        "  </tbody>\n",
186 |        "</table>\n",
187 |        "</div>"
188 |       ],
189 |       "text/plain": [
190 |        "   CreditScore Geography Gender  Age  Tenure  Balance  NumOfProducts  \\\n",
191 |        "0          600    France   Male   40       3    60000              2   \n",
192 |        "\n",
193 |        "   HasCrCard  IsActiveMember  EstimatedSalary  \n",
194 |        "0          1               1            50000  "
195 |       ]
196 |      },
197 |      "execution_count": 9,
198 |      "metadata": {},
199 |      "output_type": "execute_result"
200 |     }
201 |    ],
202 |    "source": [
203 |     "input_df=pd.DataFrame([input_data])\n",
204 |     "input_df"
205 |    ]
206 |   },
207 |   {
208 |    "cell_type": "code",
209 |    "execution_count": 10,
210 |    "metadata": {},
211 |    "outputs": [
212 |     {
213 |      "data": {
214 |       "text/html": [
215 |        "<div>\n",
216 |        "<style scoped>\n",
217 |        "    .dataframe tbody tr th:only-of-type {\n",
218 |        "        vertical-align: middle;\n",
219 |        "    }\n",
220 |        "\n",
221 |        "    .dataframe tbody tr th {\n",
222 |        "        vertical-align: top;\n",
223 |        "    }\n",
224 |        "\n",
225 |        "    .dataframe thead th {\n",
226 |        "        text-align: right;\n",
227 |        "    }\n",
228 |        "</style>\n",
229 |        "<table border=\"1\" class=\"dataframe\">\n",
230 |        "  <thead>\n",
231 |        "    <tr style=\"text-align: right;\">\n",
232 |        "      <th></th>\n",
233 |        "      <th>CreditScore</th>\n",
234 |        "      <th>Geography</th>\n",
235 |        "      <th>Gender</th>\n",
236 |        "      <th>Age</th>\n",
237 |        "      <th>Tenure</th>\n",
238 |        "      <th>Balance</th>\n",
239 |        "      <th>NumOfProducts</th>\n",
240 |        "      <th>HasCrCard</th>\n",
241 |        "      <th>IsActiveMember</th>\n",
242 |        "      <th>EstimatedSalary</th>\n",
243 |        "    </tr>\n",
244 |        "  </thead>\n",
245 |        "  <tbody>\n",
246 |        "    <tr>\n",
247 |        "      <th>0</th>\n",
248 |        "      <td>600</td>\n",
249 |        "      <td>France</td>\n",
250 |        "      <td>1</td>\n",
251 |        "      <td>40</td>\n",
252 |        "      <td>3</td>\n",
253 |        "      <td>60000</td>\n",
254 |        "      <td>2</td>\n",
255 |        "      <td>1</td>\n",
256 |        "      <td>1</td>\n",
257 |        "      <td>50000</td>\n",
258 |        "    </tr>\n",
259 |        "  </tbody>\n",
260 |        "</table>\n",
261 |        "</div>"
262 |       ],
263 |       "text/plain": [
264 |        "   CreditScore Geography  Gender  Age  Tenure  Balance  NumOfProducts  \\\n",
265 |        "0          600    France       1   40       3    60000              2   \n",
266 |        "\n",
267 |        "   HasCrCard  IsActiveMember  EstimatedSalary  \n",
268 |        "0          1               1            50000  "
269 |       ]
270 |      },
271 |      "execution_count": 10,
272 |      "metadata": {},
273 |      "output_type": "execute_result"
274 |     }
275 |    ],
276 |    "source": [
277 |     "## Encode categorical variables\n",
278 |     "input_df['Gender']=label_encoder_gender.transform(input_df['Gender'])\n",
279 |     "input_df"
280 |    ]
281 |   },
282 |   {
283 |    "cell_type": "code",
284 |    "execution_count": 11,
285 |    "metadata": {},
286 |    "outputs": [
287 |     {
288 |      "data": {
289 |       "text/html": [
290 |        "<div>\n",
291 |        "<style scoped>\n",
292 |        "    .dataframe tbody tr th:only-of-type {\n",
293 |        "        vertical-align: middle;\n",
294 |        "    }\n",
295 |        "\n",
296 |        "    .dataframe tbody tr th {\n",
297 |        "        vertical-align: top;\n",
298 |        "    }\n",
299 |        "\n",
300 |        "    .dataframe thead th {\n",
301 |        "        text-align: right;\n",
302 |        "    }\n",
303 |        "</style>\n",
304 |        "<table border=\"1\" class=\"dataframe\">\n",
305 |        "  <thead>\n",
306 |        "    <tr style=\"text-align: right;\">\n",
307 |        "      <th></th>\n",
308 |        "      <th>CreditScore</th>\n",
309 |        "      <th>Gender</th>\n",
310 |        "      <th>Age</th>\n",
311 |        "      <th>Tenure</th>\n",
312 |        "      <th>Balance</th>\n",
313 |        "      <th>NumOfProducts</th>\n",
314 |        "      <th>HasCrCard</th>\n",
315 |        "      <th>IsActiveMember</th>\n",
316 |        "      <th>EstimatedSalary</th>\n",
317 |        "      <th>Geography_France</th>\n",
318 |        "      <th>Geography_Germany</th>\n",
319 |        "      <th>Geography_Spain</th>\n",
320 |        "    </tr>\n",
321 |        "  </thead>\n",
322 |        "  <tbody>\n",
323 |        "    <tr>\n",
324 |        "      <th>0</th>\n",
325 |        "      <td>600</td>\n",
326 |        "      <td>1</td>\n",
327 |        "      <td>40</td>\n",
328 |        "      <td>3</td>\n",
329 |        "      <td>60000</td>\n",
330 |        "      <td>2</td>\n",
331 |        "      <td>1</td>\n",
332 |        "      <td>1</td>\n",
333 |        "      <td>50000</td>\n",
334 |        "      <td>1.0</td>\n",
335 |        "      <td>0.0</td>\n",
336 |        "      <td>0.0</td>\n",
337 |        "    </tr>\n",
338 |        "  </tbody>\n",
339 |        "</table>\n",
340 |        "</div>"
341 |       ],
342 |       "text/plain": [
343 |        "   CreditScore  Gender  Age  Tenure  Balance  NumOfProducts  HasCrCard  \\\n",
344 |        "0          600       1   40       3    60000              2          1   \n",
345 |        "\n",
346 |        "   IsActiveMember  EstimatedSalary  Geography_France  Geography_Germany  \\\n",
347 |        "0               1            50000               1.0                0.0   \n",
348 |        "\n",
349 |        "   Geography_Spain  \n",
350 |        "0              0.0  "
351 |       ]
352 |      },
353 |      "execution_count": 11,
354 |      "metadata": {},
355 |      "output_type": "execute_result"
356 |     }
357 |    ],
358 |    "source": [
359 |     "## concatination one hot encoded \n",
360 |     "input_df=pd.concat([input_df.drop(\"Geography\",axis=1),geo_encoded_df],axis=1)\n",
361 |     "input_df"
362 |    ]
363 |   },
364 |   {
365 |    "cell_type": "code",
366 |    "execution_count": 12,
367 |    "metadata": {},
368 |    "outputs": [
369 |     {
370 |      "data": {
371 |       "text/plain": [
372 |        "array([[-0.53598516,  0.91324755,  0.10479359, -0.69539349, -0.25781119,\n",
373 |        "         0.80843615,  0.64920267,  0.97481699, -0.87683221,  1.00150113,\n",
374 |        "        -0.57946723, -0.57638802]])"
375 |       ]
376 |      },
377 |      "execution_count": 12,
378 |      "metadata": {},
379 |      "output_type": "execute_result"
380 |     }
381 |    ],
382 |    "source": [
383 |     "## Scaling the input data\n",
384 |     "input_scaled=scaler.transform(input_df)\n",
385 |     "input_scaled"
386 |    ]
387 |   },
388 |   {
389 |    "cell_type": "code",
390 |    "execution_count": 13,
391 |    "metadata": {},
392 |    "outputs": [
393 |     {
394 |      "name": "stdout",
395 |      "output_type": "stream",
396 |      "text": [
397 |       "1/1 [==============================] - 0s 94ms/step\n"
398 |      ]
399 |     },
400 |     {
401 |      "data": {
402 |       "text/plain": [
403 |        "array([[0.02973952]], dtype=float32)"
404 |       ]
405 |      },
406 |      "execution_count": 13,
407 |      "metadata": {},
408 |      "output_type": "execute_result"
409 |     }
410 |    ],
411 |    "source": [
412 |     "## PRedict churn\n",
413 |     "prediction=model.predict(input_scaled)\n",
414 |     "prediction"
415 |    ]
416 |   },
417 |   {
418 |    "cell_type": "code",
419 |    "execution_count": 14,
420 |    "metadata": {},
421 |    "outputs": [],
422 |    "source": [
423 |     "prediction_proba = prediction[0][0]"
424 |    ]
425 |   },
426 |   {
427 |    "cell_type": "code",
428 |    "execution_count": 15,
429 |    "metadata": {},
430 |    "outputs": [
431 |     {
432 |      "data": {
433 |       "text/plain": [
434 |        "0.029739516"
435 |       ]
436 |      },
437 |      "execution_count": 15,
438 |      "metadata": {},
439 |      "output_type": "execute_result"
440 |     }
441 |    ],
442 |    "source": [
443 |     "prediction_proba"
444 |    ]
445 |   },
446 |   {
447 |    "cell_type": "code",
448 |    "execution_count": 16,
449 |    "metadata": {},
450 |    "outputs": [
451 |     {
452 |      "name": "stdout",
453 |      "output_type": "stream",
454 |      "text": [
455 |       "The customer is not likely to churn.\n"
456 |      ]
457 |     }
458 |    ],
459 |    "source": [
460 |     "if prediction_proba > 0.5:\n",
461 |     "    print('The customer is likely to churn.')\n",
462 |     "else:\n",
463 |     "    print('The customer is not likely to churn.')"
464 |    ]
465 |   },
466 |   {
467 |    "cell_type": "code",
468 |    "execution_count": null,
469 |    "metadata": {},
470 |    "outputs": [],
471 |    "source": []
472 |   }
473 |  ],
474 |  "metadata": {
475 |   "kernelspec": {
476 |    "display_name": "Python 3",
477 |    "language": "python",
478 |    "name": "python3"
479 |   },
480 |   "language_info": {
481 |    "codemirror_mode": {
482 |     "name": "ipython",
483 |     "version": 3
484 |    },
485 |    "file_extension": ".py",
486 |    "mimetype": "text/x-python",
487 |    "name": "python",
488 |    "nbconvert_exporter": "python",
489 |    "pygments_lexer": "ipython3",
490 |    "version": "3.11.0"
491 |   }
492 |  },
493 |  "nbformat": 4,
494 |  "nbformat_minor": 2
495 | }
496 | 


--------------------------------------------------------------------------------
/LICENSE:
--------------------------------------------------------------------------------
  1 |                     GNU GENERAL PUBLIC LICENSE
  2 |                        Version 3, 29 June 2007
  3 | 
  4 |  Copyright (C) 2007 Free Software Foundation, Inc. <https://fsf.org/>
  5 |  Everyone is permitted to copy and distribute verbatim copies
  6 |  of this license document, but changing it is not allowed.
  7 | 
  8 |                             Preamble
  9 | 
 10 |   The GNU General Public License is a free, copyleft license for
 11 | software and other kinds of works.
 12 | 
 13 |   The licenses for most software and other practical works are designed
 14 | to take away your freedom to share and change the works.  By contrast,
 15 | the GNU General Public License is intended to guarantee your freedom to
 16 | share and change all versions of a program--to make sure it remains free
 17 | software for all its users.  We, the Free Software Foundation, use the
 18 | GNU General Public License for most of our software; it applies also to
 19 | any other work released this way by its authors.  You can apply it to
 20 | your programs, too.
 21 | 
 22 |   When we speak of free software, we are referring to freedom, not
 23 | price.  Our General Public Licenses are designed to make sure that you
 24 | have the freedom to distribute copies of free software (and charge for
 25 | them if you wish), that you receive source code or can get it if you
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 27 | free programs, and that you know you can do these things.
 28 | 
 29 |   To protect your rights, we need to prevent others from denying you
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235 |   A compilation of a covered work with other separate and independent
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245 |   6. Conveying Non-Source Forms.
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247 |   You may convey a covered work in object code form under the terms
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275 |     d) Convey the object code by offering access from a designated
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288 |     e) Convey the object code using peer-to-peer transmission, provided
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297 |   A "User Product" is either (1) a "consumer product", which means any
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318 |   If you convey an object code work under this section in, or with, or
319 | specifically for use in, a User Product, and the conveying occurs as
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342 | 
343 |   7. Additional Terms.
344 | 
345 |   "Additional permissions" are terms that supplement the terms of this
346 | License by making exceptions from one or more of its conditions.
347 | Additional permissions that are applicable to the entire Program shall
348 | be treated as though they were included in this License, to the extent
349 | that they are valid under applicable law.  If additional permissions
350 | apply only to part of the Program, that part may be used separately
351 | under those permissions, but the entire Program remains governed by
352 | this License without regard to the additional permissions.
353 | 
354 |   When you convey a copy of a covered work, you may at your option
355 | remove any additional permissions from that copy, or from any part of
356 | it.  (Additional permissions may be written to require their own
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361 |   Notwithstanding any other provision of this License, for material you
362 | add to a covered work, you may (if authorized by the copyright holders of
363 | that material) supplement the terms of this License with terms:
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365 |     a) Disclaiming warranty or limiting liability differently from the
366 |     terms of sections 15 and 16 of this License; or
367 | 
368 |     b) Requiring preservation of specified reasonable legal notices or
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388 |   All other non-permissive additional terms are considered "further
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390 | received it, or any part of it, contains a notice stating that it is
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393 | a further restriction but permits relicensing or conveying under this
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398 |   If you add terms to a covered work in accord with this section, you
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403 |   Additional terms, permissive or non-permissive, may be stated in the
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406 | 
407 |   8. Termination.
408 | 
409 |   You may not propagate or modify a covered work except as expressly
410 | provided under this License.  Any attempt otherwise to propagate or
411 | modify it is void, and will automatically terminate your rights under
412 | this License (including any patent licenses granted under the third
413 | paragraph of section 11).
414 | 
415 |   However, if you cease all violation of this License, then your
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421 | 
422 |   Moreover, your license from a particular copyright holder is
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425 | received notice of violation of this License (for any work) from that
426 | copyright holder, and you cure the violation prior to 30 days after
427 | your receipt of the notice.
428 | 
429 |   Termination of your rights under this section does not terminate the
430 | licenses of parties who have received copies or rights from you under
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432 | reinstated, you do not qualify to receive new licenses for the same
433 | material under section 10.
434 | 
435 |   9. Acceptance Not Required for Having Copies.
436 | 
437 |   You are not required to accept this License in order to receive or
438 | run a copy of the Program.  Ancillary propagation of a covered work
439 | occurring solely as a consequence of using peer-to-peer transmission
440 | to receive a copy likewise does not require acceptance.  However,
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443 | not accept this License.  Therefore, by modifying or propagating a
444 | covered work, you indicate your acceptance of this License to do so.
445 | 
446 |   10. Automatic Licensing of Downstream Recipients.
447 | 
448 |   Each time you convey a covered work, the recipient automatically
449 | receives a license from the original licensors, to run, modify and
450 | propagate that work, subject to this License.  You are not responsible
451 | for enforcing compliance by third parties with this License.
452 | 
453 |   An "entity transaction" is a transaction transferring control of an
454 | organization, or substantially all assets of one, or subdividing an
455 | organization, or merging organizations.  If propagation of a covered
456 | work results from an entity transaction, each party to that
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462 | 
463 |   You may not impose any further restrictions on the exercise of the
464 | rights granted or affirmed under this License.  For example, you may
465 | not impose a license fee, royalty, or other charge for exercise of
466 | rights granted under this License, and you may not initiate litigation
467 | (including a cross-claim or counterclaim in a lawsuit) alleging that
468 | any patent claim is infringed by making, using, selling, offering for
469 | sale, or importing the Program or any portion of it.
470 | 
471 |   11. Patents.
472 | 
473 |   A "contributor" is a copyright holder who authorizes use under this
474 | License of the Program or a work on which the Program is based.  The
475 | work thus licensed is called the contributor's "contributor version".
476 | 
477 |   A contributor's "essential patent claims" are all patent claims
478 | owned or controlled by the contributor, whether already acquired or
479 | hereafter acquired, that would be infringed by some manner, permitted
480 | by this License, of making, using, or selling its contributor version,
481 | but do not include claims that would be infringed only as a
482 | consequence of further modification of the contributor version.  For
483 | purposes of this definition, "control" includes the right to grant
484 | patent sublicenses in a manner consistent with the requirements of
485 | this License.
486 | 
487 |   Each contributor grants you a non-exclusive, worldwide, royalty-free
488 | patent license under the contributor's essential patent claims, to
489 | make, use, sell, offer for sale, import and otherwise run, modify and
490 | propagate the contents of its contributor version.
491 | 
492 |   In the following three paragraphs, a "patent license" is any express
493 | agreement or commitment, however denominated, not to enforce a patent
494 | (such as an express permission to practice a patent or covenant not to
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497 | patent against the party.
498 | 
499 |   If you convey a covered work, knowingly relying on a patent license,
500 | and the Corresponding Source of the work is not available for anyone
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503 | then you must either (1) cause the Corresponding Source to be so
504 | available, or (2) arrange to deprive yourself of the benefit of the
505 | patent license for this particular work, or (3) arrange, in a manner
506 | consistent with the requirements of this License, to extend the patent
507 | license to downstream recipients.  "Knowingly relying" means you have
508 | actual knowledge that, but for the patent license, your conveying the
509 | covered work in a country, or your recipient's use of the covered work
510 | in a country, would infringe one or more identifiable patents in that
511 | country that you have reason to believe are valid.
512 | 
513 |   If, pursuant to or in connection with a single transaction or
514 | arrangement, you convey, or propagate by procuring conveyance of, a
515 | covered work, and grant a patent license to some of the parties
516 | receiving the covered work authorizing them to use, propagate, modify
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518 | you grant is automatically extended to all recipients of the covered
519 | work and works based on it.
520 | 
521 |   A patent license is "discriminatory" if it does not include within
522 | the scope of its coverage, prohibits the exercise of, or is
523 | conditioned on the non-exercise of one or more of the rights that are
524 | specifically granted under this License.  You may not convey a covered
525 | work if you are a party to an arrangement with a third party that is
526 | in the business of distributing software, under which you make payment
527 | to the third party based on the extent of your activity of conveying
528 | the work, and under which the third party grants, to any of the
529 | parties who would receive the covered work from you, a discriminatory
530 | patent license (a) in connection with copies of the covered work
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533 | contain the covered work, unless you entered into that arrangement,
534 | or that patent license was granted, prior to 28 March 2007.
535 | 
536 |   Nothing in this License shall be construed as excluding or limiting
537 | any implied license or other defenses to infringement that may
538 | otherwise be available to you under applicable patent law.
539 | 
540 |   12. No Surrender of Others' Freedom.
541 | 
542 |   If conditions are imposed on you (whether by court order, agreement or
543 | otherwise) that contradict the conditions of this License, they do not
544 | excuse you from the conditions of this License.  If you cannot convey a
545 | covered work so as to satisfy simultaneously your obligations under this
546 | License and any other pertinent obligations, then as a consequence you may
547 | not convey it at all.  For example, if you agree to terms that obligate you
548 | to collect a royalty for further conveying from those to whom you convey
549 | the Program, the only way you could satisfy both those terms and this
550 | License would be to refrain entirely from conveying the Program.
551 | 
552 |   13. Use with the GNU Affero General Public License.
553 | 
554 |   Notwithstanding any other provision of this License, you have
555 | permission to link or combine any covered work with a work licensed
556 | under version 3 of the GNU Affero General Public License into a single
557 | combined work, and to convey the resulting work.  The terms of this
558 | License will continue to apply to the part which is the covered work,
559 | but the special requirements of the GNU Affero General Public License,
560 | section 13, concerning interaction through a network will apply to the
561 | combination as such.
562 | 
563 |   14. Revised Versions of this License.
564 | 
565 |   The Free Software Foundation may publish revised and/or new versions of
566 | the GNU General Public License from time to time.  Such new versions will
567 | be similar in spirit to the present version, but may differ in detail to
568 | address new problems or concerns.
569 | 
570 |   Each version is given a distinguishing version number.  If the
571 | Program specifies that a certain numbered version of the GNU General
572 | Public License "or any later version" applies to it, you have the
573 | option of following the terms and conditions either of that numbered
574 | version or of any later version published by the Free Software
575 | Foundation.  If the Program does not specify a version number of the
576 | GNU General Public License, you may choose any version ever published
577 | by the Free Software Foundation.
578 | 
579 |   If the Program specifies that a proxy can decide which future
580 | versions of the GNU General Public License can be used, that proxy's
581 | public statement of acceptance of a version permanently authorizes you
582 | to choose that version for the Program.
583 | 
584 |   Later license versions may give you additional or different
585 | permissions.  However, no additional obligations are imposed on any
586 | author or copyright holder as a result of your choosing to follow a
587 | later version.
588 | 
589 |   15. Disclaimer of Warranty.
590 | 
591 |   THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY
592 | APPLICABLE LAW.  EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT
593 | HOLDERS AND/OR OTHER PARTIES PROVIDE THE PROGRAM "AS IS" WITHOUT WARRANTY
594 | OF ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO,
595 | THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
596 | PURPOSE.  THE ENTIRE RISK AS TO THE QUALITY AND PERFORMANCE OF THE PROGRAM
597 | IS WITH YOU.  SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF
598 | ALL NECESSARY SERVICING, REPAIR OR CORRECTION.
599 | 
600 |   16. Limitation of Liability.
601 | 
602 |   IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING
603 | WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS
604 | THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY
605 | GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE
606 | USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF
607 | DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD
608 | PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS),
609 | EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF
610 | SUCH DAMAGES.
611 | 
612 |   17. Interpretation of Sections 15 and 16.
613 | 
614 |   If the disclaimer of warranty and limitation of liability provided
615 | above cannot be given local legal effect according to their terms,
616 | reviewing courts shall apply local law that most closely approximates
617 | an absolute waiver of all civil liability in connection with the
618 | Program, unless a warranty or assumption of liability accompanies a
619 | copy of the Program in return for a fee.
620 | 
621 |                      END OF TERMS AND CONDITIONS
622 | 
623 |             How to Apply These Terms to Your New Programs
624 | 
625 |   If you develop a new program, and you want it to be of the greatest
626 | possible use to the public, the best way to achieve this is to make it
627 | free software which everyone can redistribute and change under these terms.
628 | 
629 |   To do so, attach the following notices to the program.  It is safest
630 | to attach them to the start of each source file to most effectively
631 | state the exclusion of warranty; and each file should have at least
632 | the "copyright" line and a pointer to where the full notice is found.
633 | 
634 |     <one line to give the program's name and a brief idea of what it does.>
635 |     Copyright (C) <year>  <name of author>
636 | 
637 |     This program is free software: you can redistribute it and/or modify
638 |     it under the terms of the GNU General Public License as published by
639 |     the Free Software Foundation, either version 3 of the License, or
640 |     (at your option) any later version.
641 | 
642 |     This program is distributed in the hope that it will be useful,
643 |     but WITHOUT ANY WARRANTY; without even the implied warranty of
644 |     MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
645 |     GNU General Public License for more details.
646 | 
647 |     You should have received a copy of the GNU General Public License
648 |     along with this program.  If not, see <https://www.gnu.org/licenses/>.
649 | 
650 | Also add information on how to contact you by electronic and paper mail.
651 | 
652 |   If the program does terminal interaction, make it output a short
653 | notice like this when it starts in an interactive mode:
654 | 
655 |     <program>  Copyright (C) <year>  <name of author>
656 |     This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'.
657 |     This is free software, and you are welcome to redistribute it
658 |     under certain conditions; type `show c' for details.
659 | 
660 | The hypothetical commands `show w' and `show c' should show the appropriate
661 | parts of the General Public License.  Of course, your program's commands
662 | might be different; for a GUI interface, you would use an "about box".
663 | 
664 |   You should also get your employer (if you work as a programmer) or school,
665 | if any, to sign a "copyright disclaimer" for the program, if necessary.
666 | For more information on this, and how to apply and follow the GNU GPL, see
667 | <https://www.gnu.org/licenses/>.
668 | 
669 |   The GNU General Public License does not permit incorporating your program
670 | into proprietary programs.  If your program is a subroutine library, you
671 | may consider it more useful to permit linking proprietary applications with
672 | the library.  If this is what you want to do, use the GNU Lesser General
673 | Public License instead of this License.  But first, please read
674 | <https://www.gnu.org/licenses/why-not-lgpl.html>.
675 | 


--------------------------------------------------------------------------------
/experiments.ipynb:
--------------------------------------------------------------------------------
   1 | {
   2 |  "cells": [
   3 |   {
   4 |    "cell_type": "code",
   5 |    "execution_count": 33,
   6 |    "metadata": {},
   7 |    "outputs": [],
   8 |    "source": [
   9 |     "import pandas as pd\n",
  10 |     "from sklearn.model_selection import train_test_split\n",
  11 |     "from sklearn.preprocessing import StandardScaler,LabelEncoder\n",
  12 |     "import pickle"
  13 |    ]
  14 |   },
  15 |   {
  16 |    "cell_type": "code",
  17 |    "execution_count": 34,
  18 |    "metadata": {},
  19 |    "outputs": [
  20 |     {
  21 |      "data": {
  22 |       "text/html": [
  23 |        "<div>\n",
  24 |        "<style scoped>\n",
  25 |        "    .dataframe tbody tr th:only-of-type {\n",
  26 |        "        vertical-align: middle;\n",
  27 |        "    }\n",
  28 |        "\n",
  29 |        "    .dataframe tbody tr th {\n",
  30 |        "        vertical-align: top;\n",
  31 |        "    }\n",
  32 |        "\n",
  33 |        "    .dataframe thead th {\n",
  34 |        "        text-align: right;\n",
  35 |        "    }\n",
  36 |        "</style>\n",
  37 |        "<table border=\"1\" class=\"dataframe\">\n",
  38 |        "  <thead>\n",
  39 |        "    <tr style=\"text-align: right;\">\n",
  40 |        "      <th></th>\n",
  41 |        "      <th>RowNumber</th>\n",
  42 |        "      <th>CustomerId</th>\n",
  43 |        "      <th>Surname</th>\n",
  44 |        "      <th>CreditScore</th>\n",
  45 |        "      <th>Geography</th>\n",
  46 |        "      <th>Gender</th>\n",
  47 |        "      <th>Age</th>\n",
  48 |        "      <th>Tenure</th>\n",
  49 |        "      <th>Balance</th>\n",
  50 |        "      <th>NumOfProducts</th>\n",
  51 |        "      <th>HasCrCard</th>\n",
  52 |        "      <th>IsActiveMember</th>\n",
  53 |        "      <th>EstimatedSalary</th>\n",
  54 |        "      <th>Exited</th>\n",
  55 |        "    </tr>\n",
  56 |        "  </thead>\n",
  57 |        "  <tbody>\n",
  58 |        "    <tr>\n",
  59 |        "      <th>0</th>\n",
  60 |        "      <td>1</td>\n",
  61 |        "      <td>15634602</td>\n",
  62 |        "      <td>Hargrave</td>\n",
  63 |        "      <td>619</td>\n",
  64 |        "      <td>France</td>\n",
  65 |        "      <td>Female</td>\n",
  66 |        "      <td>42</td>\n",
  67 |        "      <td>2</td>\n",
  68 |        "      <td>0.00</td>\n",
  69 |        "      <td>1</td>\n",
  70 |        "      <td>1</td>\n",
  71 |        "      <td>1</td>\n",
  72 |        "      <td>101348.88</td>\n",
  73 |        "      <td>1</td>\n",
  74 |        "    </tr>\n",
  75 |        "    <tr>\n",
  76 |        "      <th>1</th>\n",
  77 |        "      <td>2</td>\n",
  78 |        "      <td>15647311</td>\n",
  79 |        "      <td>Hill</td>\n",
  80 |        "      <td>608</td>\n",
  81 |        "      <td>Spain</td>\n",
  82 |        "      <td>Female</td>\n",
  83 |        "      <td>41</td>\n",
  84 |        "      <td>1</td>\n",
  85 |        "      <td>83807.86</td>\n",
  86 |        "      <td>1</td>\n",
  87 |        "      <td>0</td>\n",
  88 |        "      <td>1</td>\n",
  89 |        "      <td>112542.58</td>\n",
  90 |        "      <td>0</td>\n",
  91 |        "    </tr>\n",
  92 |        "    <tr>\n",
  93 |        "      <th>2</th>\n",
  94 |        "      <td>3</td>\n",
  95 |        "      <td>15619304</td>\n",
  96 |        "      <td>Onio</td>\n",
  97 |        "      <td>502</td>\n",
  98 |        "      <td>France</td>\n",
  99 |        "      <td>Female</td>\n",
 100 |        "      <td>42</td>\n",
 101 |        "      <td>8</td>\n",
 102 |        "      <td>159660.80</td>\n",
 103 |        "      <td>3</td>\n",
 104 |        "      <td>1</td>\n",
 105 |        "      <td>0</td>\n",
 106 |        "      <td>113931.57</td>\n",
 107 |        "      <td>1</td>\n",
 108 |        "    </tr>\n",
 109 |        "    <tr>\n",
 110 |        "      <th>3</th>\n",
 111 |        "      <td>4</td>\n",
 112 |        "      <td>15701354</td>\n",
 113 |        "      <td>Boni</td>\n",
 114 |        "      <td>699</td>\n",
 115 |        "      <td>France</td>\n",
 116 |        "      <td>Female</td>\n",
 117 |        "      <td>39</td>\n",
 118 |        "      <td>1</td>\n",
 119 |        "      <td>0.00</td>\n",
 120 |        "      <td>2</td>\n",
 121 |        "      <td>0</td>\n",
 122 |        "      <td>0</td>\n",
 123 |        "      <td>93826.63</td>\n",
 124 |        "      <td>0</td>\n",
 125 |        "    </tr>\n",
 126 |        "    <tr>\n",
 127 |        "      <th>4</th>\n",
 128 |        "      <td>5</td>\n",
 129 |        "      <td>15737888</td>\n",
 130 |        "      <td>Mitchell</td>\n",
 131 |        "      <td>850</td>\n",
 132 |        "      <td>Spain</td>\n",
 133 |        "      <td>Female</td>\n",
 134 |        "      <td>43</td>\n",
 135 |        "      <td>2</td>\n",
 136 |        "      <td>125510.82</td>\n",
 137 |        "      <td>1</td>\n",
 138 |        "      <td>1</td>\n",
 139 |        "      <td>1</td>\n",
 140 |        "      <td>79084.10</td>\n",
 141 |        "      <td>0</td>\n",
 142 |        "    </tr>\n",
 143 |        "  </tbody>\n",
 144 |        "</table>\n",
 145 |        "</div>"
 146 |       ],
 147 |       "text/plain": [
 148 |        "   RowNumber  CustomerId   Surname  CreditScore Geography  Gender  Age  \\\n",
 149 |        "0          1    15634602  Hargrave          619    France  Female   42   \n",
 150 |        "1          2    15647311      Hill          608     Spain  Female   41   \n",
 151 |        "2          3    15619304      Onio          502    France  Female   42   \n",
 152 |        "3          4    15701354      Boni          699    France  Female   39   \n",
 153 |        "4          5    15737888  Mitchell          850     Spain  Female   43   \n",
 154 |        "\n",
 155 |        "   Tenure    Balance  NumOfProducts  HasCrCard  IsActiveMember  \\\n",
 156 |        "0       2       0.00              1          1               1   \n",
 157 |        "1       1   83807.86              1          0               1   \n",
 158 |        "2       8  159660.80              3          1               0   \n",
 159 |        "3       1       0.00              2          0               0   \n",
 160 |        "4       2  125510.82              1          1               1   \n",
 161 |        "\n",
 162 |        "   EstimatedSalary  Exited  \n",
 163 |        "0        101348.88       1  \n",
 164 |        "1        112542.58       0  \n",
 165 |        "2        113931.57       1  \n",
 166 |        "3         93826.63       0  \n",
 167 |        "4         79084.10       0  "
 168 |       ]
 169 |      },
 170 |      "execution_count": 34,
 171 |      "metadata": {},
 172 |      "output_type": "execute_result"
 173 |     }
 174 |    ],
 175 |    "source": [
 176 |     "## Load the dataset\n",
 177 |     "data=pd.read_csv(\"Churn_Modelling.csv\")\n",
 178 |     "data.head()"
 179 |    ]
 180 |   },
 181 |   {
 182 |    "cell_type": "code",
 183 |    "execution_count": 35,
 184 |    "metadata": {},
 185 |    "outputs": [
 186 |     {
 187 |      "data": {
 188 |       "text/html": [
 189 |        "<div>\n",
 190 |        "<style scoped>\n",
 191 |        "    .dataframe tbody tr th:only-of-type {\n",
 192 |        "        vertical-align: middle;\n",
 193 |        "    }\n",
 194 |        "\n",
 195 |        "    .dataframe tbody tr th {\n",
 196 |        "        vertical-align: top;\n",
 197 |        "    }\n",
 198 |        "\n",
 199 |        "    .dataframe thead th {\n",
 200 |        "        text-align: right;\n",
 201 |        "    }\n",
 202 |        "</style>\n",
 203 |        "<table border=\"1\" class=\"dataframe\">\n",
 204 |        "  <thead>\n",
 205 |        "    <tr style=\"text-align: right;\">\n",
 206 |        "      <th></th>\n",
 207 |        "      <th>CreditScore</th>\n",
 208 |        "      <th>Geography</th>\n",
 209 |        "      <th>Gender</th>\n",
 210 |        "      <th>Age</th>\n",
 211 |        "      <th>Tenure</th>\n",
 212 |        "      <th>Balance</th>\n",
 213 |        "      <th>NumOfProducts</th>\n",
 214 |        "      <th>HasCrCard</th>\n",
 215 |        "      <th>IsActiveMember</th>\n",
 216 |        "      <th>EstimatedSalary</th>\n",
 217 |        "      <th>Exited</th>\n",
 218 |        "    </tr>\n",
 219 |        "  </thead>\n",
 220 |        "  <tbody>\n",
 221 |        "    <tr>\n",
 222 |        "      <th>0</th>\n",
 223 |        "      <td>619</td>\n",
 224 |        "      <td>France</td>\n",
 225 |        "      <td>Female</td>\n",
 226 |        "      <td>42</td>\n",
 227 |        "      <td>2</td>\n",
 228 |        "      <td>0.00</td>\n",
 229 |        "      <td>1</td>\n",
 230 |        "      <td>1</td>\n",
 231 |        "      <td>1</td>\n",
 232 |        "      <td>101348.88</td>\n",
 233 |        "      <td>1</td>\n",
 234 |        "    </tr>\n",
 235 |        "    <tr>\n",
 236 |        "      <th>1</th>\n",
 237 |        "      <td>608</td>\n",
 238 |        "      <td>Spain</td>\n",
 239 |        "      <td>Female</td>\n",
 240 |        "      <td>41</td>\n",
 241 |        "      <td>1</td>\n",
 242 |        "      <td>83807.86</td>\n",
 243 |        "      <td>1</td>\n",
 244 |        "      <td>0</td>\n",
 245 |        "      <td>1</td>\n",
 246 |        "      <td>112542.58</td>\n",
 247 |        "      <td>0</td>\n",
 248 |        "    </tr>\n",
 249 |        "    <tr>\n",
 250 |        "      <th>2</th>\n",
 251 |        "      <td>502</td>\n",
 252 |        "      <td>France</td>\n",
 253 |        "      <td>Female</td>\n",
 254 |        "      <td>42</td>\n",
 255 |        "      <td>8</td>\n",
 256 |        "      <td>159660.80</td>\n",
 257 |        "      <td>3</td>\n",
 258 |        "      <td>1</td>\n",
 259 |        "      <td>0</td>\n",
 260 |        "      <td>113931.57</td>\n",
 261 |        "      <td>1</td>\n",
 262 |        "    </tr>\n",
 263 |        "    <tr>\n",
 264 |        "      <th>3</th>\n",
 265 |        "      <td>699</td>\n",
 266 |        "      <td>France</td>\n",
 267 |        "      <td>Female</td>\n",
 268 |        "      <td>39</td>\n",
 269 |        "      <td>1</td>\n",
 270 |        "      <td>0.00</td>\n",
 271 |        "      <td>2</td>\n",
 272 |        "      <td>0</td>\n",
 273 |        "      <td>0</td>\n",
 274 |        "      <td>93826.63</td>\n",
 275 |        "      <td>0</td>\n",
 276 |        "    </tr>\n",
 277 |        "    <tr>\n",
 278 |        "      <th>4</th>\n",
 279 |        "      <td>850</td>\n",
 280 |        "      <td>Spain</td>\n",
 281 |        "      <td>Female</td>\n",
 282 |        "      <td>43</td>\n",
 283 |        "      <td>2</td>\n",
 284 |        "      <td>125510.82</td>\n",
 285 |        "      <td>1</td>\n",
 286 |        "      <td>1</td>\n",
 287 |        "      <td>1</td>\n",
 288 |        "      <td>79084.10</td>\n",
 289 |        "      <td>0</td>\n",
 290 |        "    </tr>\n",
 291 |        "    <tr>\n",
 292 |        "      <th>...</th>\n",
 293 |        "      <td>...</td>\n",
 294 |        "      <td>...</td>\n",
 295 |        "      <td>...</td>\n",
 296 |        "      <td>...</td>\n",
 297 |        "      <td>...</td>\n",
 298 |        "      <td>...</td>\n",
 299 |        "      <td>...</td>\n",
 300 |        "      <td>...</td>\n",
 301 |        "      <td>...</td>\n",
 302 |        "      <td>...</td>\n",
 303 |        "      <td>...</td>\n",
 304 |        "    </tr>\n",
 305 |        "    <tr>\n",
 306 |        "      <th>9995</th>\n",
 307 |        "      <td>771</td>\n",
 308 |        "      <td>France</td>\n",
 309 |        "      <td>Male</td>\n",
 310 |        "      <td>39</td>\n",
 311 |        "      <td>5</td>\n",
 312 |        "      <td>0.00</td>\n",
 313 |        "      <td>2</td>\n",
 314 |        "      <td>1</td>\n",
 315 |        "      <td>0</td>\n",
 316 |        "      <td>96270.64</td>\n",
 317 |        "      <td>0</td>\n",
 318 |        "    </tr>\n",
 319 |        "    <tr>\n",
 320 |        "      <th>9996</th>\n",
 321 |        "      <td>516</td>\n",
 322 |        "      <td>France</td>\n",
 323 |        "      <td>Male</td>\n",
 324 |        "      <td>35</td>\n",
 325 |        "      <td>10</td>\n",
 326 |        "      <td>57369.61</td>\n",
 327 |        "      <td>1</td>\n",
 328 |        "      <td>1</td>\n",
 329 |        "      <td>1</td>\n",
 330 |        "      <td>101699.77</td>\n",
 331 |        "      <td>0</td>\n",
 332 |        "    </tr>\n",
 333 |        "    <tr>\n",
 334 |        "      <th>9997</th>\n",
 335 |        "      <td>709</td>\n",
 336 |        "      <td>France</td>\n",
 337 |        "      <td>Female</td>\n",
 338 |        "      <td>36</td>\n",
 339 |        "      <td>7</td>\n",
 340 |        "      <td>0.00</td>\n",
 341 |        "      <td>1</td>\n",
 342 |        "      <td>0</td>\n",
 343 |        "      <td>1</td>\n",
 344 |        "      <td>42085.58</td>\n",
 345 |        "      <td>1</td>\n",
 346 |        "    </tr>\n",
 347 |        "    <tr>\n",
 348 |        "      <th>9998</th>\n",
 349 |        "      <td>772</td>\n",
 350 |        "      <td>Germany</td>\n",
 351 |        "      <td>Male</td>\n",
 352 |        "      <td>42</td>\n",
 353 |        "      <td>3</td>\n",
 354 |        "      <td>75075.31</td>\n",
 355 |        "      <td>2</td>\n",
 356 |        "      <td>1</td>\n",
 357 |        "      <td>0</td>\n",
 358 |        "      <td>92888.52</td>\n",
 359 |        "      <td>1</td>\n",
 360 |        "    </tr>\n",
 361 |        "    <tr>\n",
 362 |        "      <th>9999</th>\n",
 363 |        "      <td>792</td>\n",
 364 |        "      <td>France</td>\n",
 365 |        "      <td>Female</td>\n",
 366 |        "      <td>28</td>\n",
 367 |        "      <td>4</td>\n",
 368 |        "      <td>130142.79</td>\n",
 369 |        "      <td>1</td>\n",
 370 |        "      <td>1</td>\n",
 371 |        "      <td>0</td>\n",
 372 |        "      <td>38190.78</td>\n",
 373 |        "      <td>0</td>\n",
 374 |        "    </tr>\n",
 375 |        "  </tbody>\n",
 376 |        "</table>\n",
 377 |        "<p>10000 rows × 11 columns</p>\n",
 378 |        "</div>"
 379 |       ],
 380 |       "text/plain": [
 381 |        "      CreditScore Geography  Gender  Age  Tenure    Balance  NumOfProducts  \\\n",
 382 |        "0             619    France  Female   42       2       0.00              1   \n",
 383 |        "1             608     Spain  Female   41       1   83807.86              1   \n",
 384 |        "2             502    France  Female   42       8  159660.80              3   \n",
 385 |        "3             699    France  Female   39       1       0.00              2   \n",
 386 |        "4             850     Spain  Female   43       2  125510.82              1   \n",
 387 |        "...           ...       ...     ...  ...     ...        ...            ...   \n",
 388 |        "9995          771    France    Male   39       5       0.00              2   \n",
 389 |        "9996          516    France    Male   35      10   57369.61              1   \n",
 390 |        "9997          709    France  Female   36       7       0.00              1   \n",
 391 |        "9998          772   Germany    Male   42       3   75075.31              2   \n",
 392 |        "9999          792    France  Female   28       4  130142.79              1   \n",
 393 |        "\n",
 394 |        "      HasCrCard  IsActiveMember  EstimatedSalary  Exited  \n",
 395 |        "0             1               1        101348.88       1  \n",
 396 |        "1             0               1        112542.58       0  \n",
 397 |        "2             1               0        113931.57       1  \n",
 398 |        "3             0               0         93826.63       0  \n",
 399 |        "4             1               1         79084.10       0  \n",
 400 |        "...         ...             ...              ...     ...  \n",
 401 |        "9995          1               0         96270.64       0  \n",
 402 |        "9996          1               1        101699.77       0  \n",
 403 |        "9997          0               1         42085.58       1  \n",
 404 |        "9998          1               0         92888.52       1  \n",
 405 |        "9999          1               0         38190.78       0  \n",
 406 |        "\n",
 407 |        "[10000 rows x 11 columns]"
 408 |       ]
 409 |      },
 410 |      "execution_count": 35,
 411 |      "metadata": {},
 412 |      "output_type": "execute_result"
 413 |     }
 414 |    ],
 415 |    "source": [
 416 |     "## Preprocess the data\n",
 417 |     "### Drop irrelevant columns\n",
 418 |     "data=data.drop(['RowNumber','CustomerId','Surname'],axis=1)\n",
 419 |     "data"
 420 |    ]
 421 |   },
 422 |   {
 423 |    "cell_type": "code",
 424 |    "execution_count": 36,
 425 |    "metadata": {},
 426 |    "outputs": [
 427 |     {
 428 |      "data": {
 429 |       "text/html": [
 430 |        "<div>\n",
 431 |        "<style scoped>\n",
 432 |        "    .dataframe tbody tr th:only-of-type {\n",
 433 |        "        vertical-align: middle;\n",
 434 |        "    }\n",
 435 |        "\n",
 436 |        "    .dataframe tbody tr th {\n",
 437 |        "        vertical-align: top;\n",
 438 |        "    }\n",
 439 |        "\n",
 440 |        "    .dataframe thead th {\n",
 441 |        "        text-align: right;\n",
 442 |        "    }\n",
 443 |        "</style>\n",
 444 |        "<table border=\"1\" class=\"dataframe\">\n",
 445 |        "  <thead>\n",
 446 |        "    <tr style=\"text-align: right;\">\n",
 447 |        "      <th></th>\n",
 448 |        "      <th>CreditScore</th>\n",
 449 |        "      <th>Geography</th>\n",
 450 |        "      <th>Gender</th>\n",
 451 |        "      <th>Age</th>\n",
 452 |        "      <th>Tenure</th>\n",
 453 |        "      <th>Balance</th>\n",
 454 |        "      <th>NumOfProducts</th>\n",
 455 |        "      <th>HasCrCard</th>\n",
 456 |        "      <th>IsActiveMember</th>\n",
 457 |        "      <th>EstimatedSalary</th>\n",
 458 |        "      <th>Exited</th>\n",
 459 |        "    </tr>\n",
 460 |        "  </thead>\n",
 461 |        "  <tbody>\n",
 462 |        "    <tr>\n",
 463 |        "      <th>0</th>\n",
 464 |        "      <td>619</td>\n",
 465 |        "      <td>France</td>\n",
 466 |        "      <td>0</td>\n",
 467 |        "      <td>42</td>\n",
 468 |        "      <td>2</td>\n",
 469 |        "      <td>0.00</td>\n",
 470 |        "      <td>1</td>\n",
 471 |        "      <td>1</td>\n",
 472 |        "      <td>1</td>\n",
 473 |        "      <td>101348.88</td>\n",
 474 |        "      <td>1</td>\n",
 475 |        "    </tr>\n",
 476 |        "    <tr>\n",
 477 |        "      <th>1</th>\n",
 478 |        "      <td>608</td>\n",
 479 |        "      <td>Spain</td>\n",
 480 |        "      <td>0</td>\n",
 481 |        "      <td>41</td>\n",
 482 |        "      <td>1</td>\n",
 483 |        "      <td>83807.86</td>\n",
 484 |        "      <td>1</td>\n",
 485 |        "      <td>0</td>\n",
 486 |        "      <td>1</td>\n",
 487 |        "      <td>112542.58</td>\n",
 488 |        "      <td>0</td>\n",
 489 |        "    </tr>\n",
 490 |        "    <tr>\n",
 491 |        "      <th>2</th>\n",
 492 |        "      <td>502</td>\n",
 493 |        "      <td>France</td>\n",
 494 |        "      <td>0</td>\n",
 495 |        "      <td>42</td>\n",
 496 |        "      <td>8</td>\n",
 497 |        "      <td>159660.80</td>\n",
 498 |        "      <td>3</td>\n",
 499 |        "      <td>1</td>\n",
 500 |        "      <td>0</td>\n",
 501 |        "      <td>113931.57</td>\n",
 502 |        "      <td>1</td>\n",
 503 |        "    </tr>\n",
 504 |        "    <tr>\n",
 505 |        "      <th>3</th>\n",
 506 |        "      <td>699</td>\n",
 507 |        "      <td>France</td>\n",
 508 |        "      <td>0</td>\n",
 509 |        "      <td>39</td>\n",
 510 |        "      <td>1</td>\n",
 511 |        "      <td>0.00</td>\n",
 512 |        "      <td>2</td>\n",
 513 |        "      <td>0</td>\n",
 514 |        "      <td>0</td>\n",
 515 |        "      <td>93826.63</td>\n",
 516 |        "      <td>0</td>\n",
 517 |        "    </tr>\n",
 518 |        "    <tr>\n",
 519 |        "      <th>4</th>\n",
 520 |        "      <td>850</td>\n",
 521 |        "      <td>Spain</td>\n",
 522 |        "      <td>0</td>\n",
 523 |        "      <td>43</td>\n",
 524 |        "      <td>2</td>\n",
 525 |        "      <td>125510.82</td>\n",
 526 |        "      <td>1</td>\n",
 527 |        "      <td>1</td>\n",
 528 |        "      <td>1</td>\n",
 529 |        "      <td>79084.10</td>\n",
 530 |        "      <td>0</td>\n",
 531 |        "    </tr>\n",
 532 |        "    <tr>\n",
 533 |        "      <th>...</th>\n",
 534 |        "      <td>...</td>\n",
 535 |        "      <td>...</td>\n",
 536 |        "      <td>...</td>\n",
 537 |        "      <td>...</td>\n",
 538 |        "      <td>...</td>\n",
 539 |        "      <td>...</td>\n",
 540 |        "      <td>...</td>\n",
 541 |        "      <td>...</td>\n",
 542 |        "      <td>...</td>\n",
 543 |        "      <td>...</td>\n",
 544 |        "      <td>...</td>\n",
 545 |        "    </tr>\n",
 546 |        "    <tr>\n",
 547 |        "      <th>9995</th>\n",
 548 |        "      <td>771</td>\n",
 549 |        "      <td>France</td>\n",
 550 |        "      <td>1</td>\n",
 551 |        "      <td>39</td>\n",
 552 |        "      <td>5</td>\n",
 553 |        "      <td>0.00</td>\n",
 554 |        "      <td>2</td>\n",
 555 |        "      <td>1</td>\n",
 556 |        "      <td>0</td>\n",
 557 |        "      <td>96270.64</td>\n",
 558 |        "      <td>0</td>\n",
 559 |        "    </tr>\n",
 560 |        "    <tr>\n",
 561 |        "      <th>9996</th>\n",
 562 |        "      <td>516</td>\n",
 563 |        "      <td>France</td>\n",
 564 |        "      <td>1</td>\n",
 565 |        "      <td>35</td>\n",
 566 |        "      <td>10</td>\n",
 567 |        "      <td>57369.61</td>\n",
 568 |        "      <td>1</td>\n",
 569 |        "      <td>1</td>\n",
 570 |        "      <td>1</td>\n",
 571 |        "      <td>101699.77</td>\n",
 572 |        "      <td>0</td>\n",
 573 |        "    </tr>\n",
 574 |        "    <tr>\n",
 575 |        "      <th>9997</th>\n",
 576 |        "      <td>709</td>\n",
 577 |        "      <td>France</td>\n",
 578 |        "      <td>0</td>\n",
 579 |        "      <td>36</td>\n",
 580 |        "      <td>7</td>\n",
 581 |        "      <td>0.00</td>\n",
 582 |        "      <td>1</td>\n",
 583 |        "      <td>0</td>\n",
 584 |        "      <td>1</td>\n",
 585 |        "      <td>42085.58</td>\n",
 586 |        "      <td>1</td>\n",
 587 |        "    </tr>\n",
 588 |        "    <tr>\n",
 589 |        "      <th>9998</th>\n",
 590 |        "      <td>772</td>\n",
 591 |        "      <td>Germany</td>\n",
 592 |        "      <td>1</td>\n",
 593 |        "      <td>42</td>\n",
 594 |        "      <td>3</td>\n",
 595 |        "      <td>75075.31</td>\n",
 596 |        "      <td>2</td>\n",
 597 |        "      <td>1</td>\n",
 598 |        "      <td>0</td>\n",
 599 |        "      <td>92888.52</td>\n",
 600 |        "      <td>1</td>\n",
 601 |        "    </tr>\n",
 602 |        "    <tr>\n",
 603 |        "      <th>9999</th>\n",
 604 |        "      <td>792</td>\n",
 605 |        "      <td>France</td>\n",
 606 |        "      <td>0</td>\n",
 607 |        "      <td>28</td>\n",
 608 |        "      <td>4</td>\n",
 609 |        "      <td>130142.79</td>\n",
 610 |        "      <td>1</td>\n",
 611 |        "      <td>1</td>\n",
 612 |        "      <td>0</td>\n",
 613 |        "      <td>38190.78</td>\n",
 614 |        "      <td>0</td>\n",
 615 |        "    </tr>\n",
 616 |        "  </tbody>\n",
 617 |        "</table>\n",
 618 |        "<p>10000 rows × 11 columns</p>\n",
 619 |        "</div>"
 620 |       ],
 621 |       "text/plain": [
 622 |        "      CreditScore Geography  Gender  Age  Tenure    Balance  NumOfProducts  \\\n",
 623 |        "0             619    France       0   42       2       0.00              1   \n",
 624 |        "1             608     Spain       0   41       1   83807.86              1   \n",
 625 |        "2             502    France       0   42       8  159660.80              3   \n",
 626 |        "3             699    France       0   39       1       0.00              2   \n",
 627 |        "4             850     Spain       0   43       2  125510.82              1   \n",
 628 |        "...           ...       ...     ...  ...     ...        ...            ...   \n",
 629 |        "9995          771    France       1   39       5       0.00              2   \n",
 630 |        "9996          516    France       1   35      10   57369.61              1   \n",
 631 |        "9997          709    France       0   36       7       0.00              1   \n",
 632 |        "9998          772   Germany       1   42       3   75075.31              2   \n",
 633 |        "9999          792    France       0   28       4  130142.79              1   \n",
 634 |        "\n",
 635 |        "      HasCrCard  IsActiveMember  EstimatedSalary  Exited  \n",
 636 |        "0             1               1        101348.88       1  \n",
 637 |        "1             0               1        112542.58       0  \n",
 638 |        "2             1               0        113931.57       1  \n",
 639 |        "3             0               0         93826.63       0  \n",
 640 |        "4             1               1         79084.10       0  \n",
 641 |        "...         ...             ...              ...     ...  \n",
 642 |        "9995          1               0         96270.64       0  \n",
 643 |        "9996          1               1        101699.77       0  \n",
 644 |        "9997          0               1         42085.58       1  \n",
 645 |        "9998          1               0         92888.52       1  \n",
 646 |        "9999          1               0         38190.78       0  \n",
 647 |        "\n",
 648 |        "[10000 rows x 11 columns]"
 649 |       ]
 650 |      },
 651 |      "execution_count": 36,
 652 |      "metadata": {},
 653 |      "output_type": "execute_result"
 654 |     }
 655 |    ],
 656 |    "source": [
 657 |     "## Encode categorical variables\n",
 658 |     "label_encoder_gender=LabelEncoder()\n",
 659 |     "data['Gender']=label_encoder_gender.fit_transform(data['Gender'])\n",
 660 |     "data"
 661 |    ]
 662 |   },
 663 |   {
 664 |    "cell_type": "code",
 665 |    "execution_count": 37,
 666 |    "metadata": {},
 667 |    "outputs": [
 668 |     {
 669 |      "data": {
 670 |       "text/plain": [
 671 |        "array([[1., 0., 0.],\n",
 672 |        "       [0., 0., 1.],\n",
 673 |        "       [1., 0., 0.],\n",
 674 |        "       ...,\n",
 675 |        "       [1., 0., 0.],\n",
 676 |        "       [0., 1., 0.],\n",
 677 |        "       [1., 0., 0.]])"
 678 |       ]
 679 |      },
 680 |      "execution_count": 37,
 681 |      "metadata": {},
 682 |      "output_type": "execute_result"
 683 |     }
 684 |    ],
 685 |    "source": [
 686 |     "## Onehot encode 'Geography\n",
 687 |     "from sklearn.preprocessing import OneHotEncoder\n",
 688 |     "onehot_encoder_geo=OneHotEncoder()\n",
 689 |     "geo_encoder=onehot_encoder_geo.fit_transform(data[['Geography']]).toarray()\n",
 690 |     "geo_encoder"
 691 |    ]
 692 |   },
 693 |   {
 694 |    "cell_type": "code",
 695 |    "execution_count": 39,
 696 |    "metadata": {},
 697 |    "outputs": [
 698 |     {
 699 |      "data": {
 700 |       "text/plain": [
 701 |        "array(['Geography_France', 'Geography_Germany', 'Geography_Spain'],\n",
 702 |        "      dtype=object)"
 703 |       ]
 704 |      },
 705 |      "execution_count": 39,
 706 |      "metadata": {},
 707 |      "output_type": "execute_result"
 708 |     }
 709 |    ],
 710 |    "source": [
 711 |     "onehot_encoder_geo.get_feature_names_out(['Geography'])"
 712 |    ]
 713 |   },
 714 |   {
 715 |    "cell_type": "code",
 716 |    "execution_count": 41,
 717 |    "metadata": {},
 718 |    "outputs": [
 719 |     {
 720 |      "data": {
 721 |       "text/html": [
 722 |        "<div>\n",
 723 |        "<style scoped>\n",
 724 |        "    .dataframe tbody tr th:only-of-type {\n",
 725 |        "        vertical-align: middle;\n",
 726 |        "    }\n",
 727 |        "\n",
 728 |        "    .dataframe tbody tr th {\n",
 729 |        "        vertical-align: top;\n",
 730 |        "    }\n",
 731 |        "\n",
 732 |        "    .dataframe thead th {\n",
 733 |        "        text-align: right;\n",
 734 |        "    }\n",
 735 |        "</style>\n",
 736 |        "<table border=\"1\" class=\"dataframe\">\n",
 737 |        "  <thead>\n",
 738 |        "    <tr style=\"text-align: right;\">\n",
 739 |        "      <th></th>\n",
 740 |        "      <th>Geography_France</th>\n",
 741 |        "      <th>Geography_Germany</th>\n",
 742 |        "      <th>Geography_Spain</th>\n",
 743 |        "    </tr>\n",
 744 |        "  </thead>\n",
 745 |        "  <tbody>\n",
 746 |        "    <tr>\n",
 747 |        "      <th>0</th>\n",
 748 |        "      <td>1.0</td>\n",
 749 |        "      <td>0.0</td>\n",
 750 |        "      <td>0.0</td>\n",
 751 |        "    </tr>\n",
 752 |        "    <tr>\n",
 753 |        "      <th>1</th>\n",
 754 |        "      <td>0.0</td>\n",
 755 |        "      <td>0.0</td>\n",
 756 |        "      <td>1.0</td>\n",
 757 |        "    </tr>\n",
 758 |        "    <tr>\n",
 759 |        "      <th>2</th>\n",
 760 |        "      <td>1.0</td>\n",
 761 |        "      <td>0.0</td>\n",
 762 |        "      <td>0.0</td>\n",
 763 |        "    </tr>\n",
 764 |        "    <tr>\n",
 765 |        "      <th>3</th>\n",
 766 |        "      <td>1.0</td>\n",
 767 |        "      <td>0.0</td>\n",
 768 |        "      <td>0.0</td>\n",
 769 |        "    </tr>\n",
 770 |        "    <tr>\n",
 771 |        "      <th>4</th>\n",
 772 |        "      <td>0.0</td>\n",
 773 |        "      <td>0.0</td>\n",
 774 |        "      <td>1.0</td>\n",
 775 |        "    </tr>\n",
 776 |        "    <tr>\n",
 777 |        "      <th>...</th>\n",
 778 |        "      <td>...</td>\n",
 779 |        "      <td>...</td>\n",
 780 |        "      <td>...</td>\n",
 781 |        "    </tr>\n",
 782 |        "    <tr>\n",
 783 |        "      <th>9995</th>\n",
 784 |        "      <td>1.0</td>\n",
 785 |        "      <td>0.0</td>\n",
 786 |        "      <td>0.0</td>\n",
 787 |        "    </tr>\n",
 788 |        "    <tr>\n",
 789 |        "      <th>9996</th>\n",
 790 |        "      <td>1.0</td>\n",
 791 |        "      <td>0.0</td>\n",
 792 |        "      <td>0.0</td>\n",
 793 |        "    </tr>\n",
 794 |        "    <tr>\n",
 795 |        "      <th>9997</th>\n",
 796 |        "      <td>1.0</td>\n",
 797 |        "      <td>0.0</td>\n",
 798 |        "      <td>0.0</td>\n",
 799 |        "    </tr>\n",
 800 |        "    <tr>\n",
 801 |        "      <th>9998</th>\n",
 802 |        "      <td>0.0</td>\n",
 803 |        "      <td>1.0</td>\n",
 804 |        "      <td>0.0</td>\n",
 805 |        "    </tr>\n",
 806 |        "    <tr>\n",
 807 |        "      <th>9999</th>\n",
 808 |        "      <td>1.0</td>\n",
 809 |        "      <td>0.0</td>\n",
 810 |        "      <td>0.0</td>\n",
 811 |        "    </tr>\n",
 812 |        "  </tbody>\n",
 813 |        "</table>\n",
 814 |        "<p>10000 rows × 3 columns</p>\n",
 815 |        "</div>"
 816 |       ],
 817 |       "text/plain": [
 818 |        "      Geography_France  Geography_Germany  Geography_Spain\n",
 819 |        "0                  1.0                0.0              0.0\n",
 820 |        "1                  0.0                0.0              1.0\n",
 821 |        "2                  1.0                0.0              0.0\n",
 822 |        "3                  1.0                0.0              0.0\n",
 823 |        "4                  0.0                0.0              1.0\n",
 824 |        "...                ...                ...              ...\n",
 825 |        "9995               1.0                0.0              0.0\n",
 826 |        "9996               1.0                0.0              0.0\n",
 827 |        "9997               1.0                0.0              0.0\n",
 828 |        "9998               0.0                1.0              0.0\n",
 829 |        "9999               1.0                0.0              0.0\n",
 830 |        "\n",
 831 |        "[10000 rows x 3 columns]"
 832 |       ]
 833 |      },
 834 |      "execution_count": 41,
 835 |      "metadata": {},
 836 |      "output_type": "execute_result"
 837 |     }
 838 |    ],
 839 |    "source": [
 840 |     "geo_encoded_df=pd.DataFrame(geo_encoder,columns=onehot_encoder_geo.get_feature_names_out(['Geography']))\n",
 841 |     "geo_encoded_df"
 842 |    ]
 843 |   },
 844 |   {
 845 |    "cell_type": "code",
 846 |    "execution_count": 42,
 847 |    "metadata": {},
 848 |    "outputs": [
 849 |     {
 850 |      "data": {
 851 |       "text/html": [
 852 |        "<div>\n",
 853 |        "<style scoped>\n",
 854 |        "    .dataframe tbody tr th:only-of-type {\n",
 855 |        "        vertical-align: middle;\n",
 856 |        "    }\n",
 857 |        "\n",
 858 |        "    .dataframe tbody tr th {\n",
 859 |        "        vertical-align: top;\n",
 860 |        "    }\n",
 861 |        "\n",
 862 |        "    .dataframe thead th {\n",
 863 |        "        text-align: right;\n",
 864 |        "    }\n",
 865 |        "</style>\n",
 866 |        "<table border=\"1\" class=\"dataframe\">\n",
 867 |        "  <thead>\n",
 868 |        "    <tr style=\"text-align: right;\">\n",
 869 |        "      <th></th>\n",
 870 |        "      <th>CreditScore</th>\n",
 871 |        "      <th>Gender</th>\n",
 872 |        "      <th>Age</th>\n",
 873 |        "      <th>Tenure</th>\n",
 874 |        "      <th>Balance</th>\n",
 875 |        "      <th>NumOfProducts</th>\n",
 876 |        "      <th>HasCrCard</th>\n",
 877 |        "      <th>IsActiveMember</th>\n",
 878 |        "      <th>EstimatedSalary</th>\n",
 879 |        "      <th>Exited</th>\n",
 880 |        "      <th>Geography_France</th>\n",
 881 |        "      <th>Geography_Germany</th>\n",
 882 |        "      <th>Geography_Spain</th>\n",
 883 |        "    </tr>\n",
 884 |        "  </thead>\n",
 885 |        "  <tbody>\n",
 886 |        "    <tr>\n",
 887 |        "      <th>0</th>\n",
 888 |        "      <td>619</td>\n",
 889 |        "      <td>0</td>\n",
 890 |        "      <td>42</td>\n",
 891 |        "      <td>2</td>\n",
 892 |        "      <td>0.00</td>\n",
 893 |        "      <td>1</td>\n",
 894 |        "      <td>1</td>\n",
 895 |        "      <td>1</td>\n",
 896 |        "      <td>101348.88</td>\n",
 897 |        "      <td>1</td>\n",
 898 |        "      <td>1.0</td>\n",
 899 |        "      <td>0.0</td>\n",
 900 |        "      <td>0.0</td>\n",
 901 |        "    </tr>\n",
 902 |        "    <tr>\n",
 903 |        "      <th>1</th>\n",
 904 |        "      <td>608</td>\n",
 905 |        "      <td>0</td>\n",
 906 |        "      <td>41</td>\n",
 907 |        "      <td>1</td>\n",
 908 |        "      <td>83807.86</td>\n",
 909 |        "      <td>1</td>\n",
 910 |        "      <td>0</td>\n",
 911 |        "      <td>1</td>\n",
 912 |        "      <td>112542.58</td>\n",
 913 |        "      <td>0</td>\n",
 914 |        "      <td>0.0</td>\n",
 915 |        "      <td>0.0</td>\n",
 916 |        "      <td>1.0</td>\n",
 917 |        "    </tr>\n",
 918 |        "    <tr>\n",
 919 |        "      <th>2</th>\n",
 920 |        "      <td>502</td>\n",
 921 |        "      <td>0</td>\n",
 922 |        "      <td>42</td>\n",
 923 |        "      <td>8</td>\n",
 924 |        "      <td>159660.80</td>\n",
 925 |        "      <td>3</td>\n",
 926 |        "      <td>1</td>\n",
 927 |        "      <td>0</td>\n",
 928 |        "      <td>113931.57</td>\n",
 929 |        "      <td>1</td>\n",
 930 |        "      <td>1.0</td>\n",
 931 |        "      <td>0.0</td>\n",
 932 |        "      <td>0.0</td>\n",
 933 |        "    </tr>\n",
 934 |        "    <tr>\n",
 935 |        "      <th>3</th>\n",
 936 |        "      <td>699</td>\n",
 937 |        "      <td>0</td>\n",
 938 |        "      <td>39</td>\n",
 939 |        "      <td>1</td>\n",
 940 |        "      <td>0.00</td>\n",
 941 |        "      <td>2</td>\n",
 942 |        "      <td>0</td>\n",
 943 |        "      <td>0</td>\n",
 944 |        "      <td>93826.63</td>\n",
 945 |        "      <td>0</td>\n",
 946 |        "      <td>1.0</td>\n",
 947 |        "      <td>0.0</td>\n",
 948 |        "      <td>0.0</td>\n",
 949 |        "    </tr>\n",
 950 |        "    <tr>\n",
 951 |        "      <th>4</th>\n",
 952 |        "      <td>850</td>\n",
 953 |        "      <td>0</td>\n",
 954 |        "      <td>43</td>\n",
 955 |        "      <td>2</td>\n",
 956 |        "      <td>125510.82</td>\n",
 957 |        "      <td>1</td>\n",
 958 |        "      <td>1</td>\n",
 959 |        "      <td>1</td>\n",
 960 |        "      <td>79084.10</td>\n",
 961 |        "      <td>0</td>\n",
 962 |        "      <td>0.0</td>\n",
 963 |        "      <td>0.0</td>\n",
 964 |        "      <td>1.0</td>\n",
 965 |        "    </tr>\n",
 966 |        "  </tbody>\n",
 967 |        "</table>\n",
 968 |        "</div>"
 969 |       ],
 970 |       "text/plain": [
 971 |        "   CreditScore  Gender  Age  Tenure    Balance  NumOfProducts  HasCrCard  \\\n",
 972 |        "0          619       0   42       2       0.00              1          1   \n",
 973 |        "1          608       0   41       1   83807.86              1          0   \n",
 974 |        "2          502       0   42       8  159660.80              3          1   \n",
 975 |        "3          699       0   39       1       0.00              2          0   \n",
 976 |        "4          850       0   43       2  125510.82              1          1   \n",
 977 |        "\n",
 978 |        "   IsActiveMember  EstimatedSalary  Exited  Geography_France  \\\n",
 979 |        "0               1        101348.88       1               1.0   \n",
 980 |        "1               1        112542.58       0               0.0   \n",
 981 |        "2               0        113931.57       1               1.0   \n",
 982 |        "3               0         93826.63       0               1.0   \n",
 983 |        "4               1         79084.10       0               0.0   \n",
 984 |        "\n",
 985 |        "   Geography_Germany  Geography_Spain  \n",
 986 |        "0                0.0              0.0  \n",
 987 |        "1                0.0              1.0  \n",
 988 |        "2                0.0              0.0  \n",
 989 |        "3                0.0              0.0  \n",
 990 |        "4                0.0              1.0  "
 991 |       ]
 992 |      },
 993 |      "execution_count": 42,
 994 |      "metadata": {},
 995 |      "output_type": "execute_result"
 996 |     }
 997 |    ],
 998 |    "source": [
 999 |     "## Combine one hot encoder columns with the original data\n",
1000 |     "data=pd.concat([data.drop('Geography',axis=1),geo_encoded_df],axis=1)\n",
1001 |     "data.head()"
1002 |    ]
1003 |   },
1004 |   {
1005 |    "cell_type": "code",
1006 |    "execution_count": 43,
1007 |    "metadata": {},
1008 |    "outputs": [],
1009 |    "source": [
1010 |     "## Save the encoders and sscaler\n",
1011 |     "with open('label_encoder_gender.pkl','wb') as file:\n",
1012 |     "    pickle.dump(label_encoder_gender,file)\n",
1013 |     "\n",
1014 |     "with open('onehot_encoder_geo.pkl','wb') as file:\n",
1015 |     "    pickle.dump(onehot_encoder_geo,file)\n"
1016 |    ]
1017 |   },
1018 |   {
1019 |    "cell_type": "code",
1020 |    "execution_count": 44,
1021 |    "metadata": {},
1022 |    "outputs": [
1023 |     {
1024 |      "data": {
1025 |       "text/html": [
1026 |        "<div>\n",
1027 |        "<style scoped>\n",
1028 |        "    .dataframe tbody tr th:only-of-type {\n",
1029 |        "        vertical-align: middle;\n",
1030 |        "    }\n",
1031 |        "\n",
1032 |        "    .dataframe tbody tr th {\n",
1033 |        "        vertical-align: top;\n",
1034 |        "    }\n",
1035 |        "\n",
1036 |        "    .dataframe thead th {\n",
1037 |        "        text-align: right;\n",
1038 |        "    }\n",
1039 |        "</style>\n",
1040 |        "<table border=\"1\" class=\"dataframe\">\n",
1041 |        "  <thead>\n",
1042 |        "    <tr style=\"text-align: right;\">\n",
1043 |        "      <th></th>\n",
1044 |        "      <th>CreditScore</th>\n",
1045 |        "      <th>Gender</th>\n",
1046 |        "      <th>Age</th>\n",
1047 |        "      <th>Tenure</th>\n",
1048 |        "      <th>Balance</th>\n",
1049 |        "      <th>NumOfProducts</th>\n",
1050 |        "      <th>HasCrCard</th>\n",
1051 |        "      <th>IsActiveMember</th>\n",
1052 |        "      <th>EstimatedSalary</th>\n",
1053 |        "      <th>Exited</th>\n",
1054 |        "      <th>Geography_France</th>\n",
1055 |        "      <th>Geography_Germany</th>\n",
1056 |        "      <th>Geography_Spain</th>\n",
1057 |        "    </tr>\n",
1058 |        "  </thead>\n",
1059 |        "  <tbody>\n",
1060 |        "    <tr>\n",
1061 |        "      <th>0</th>\n",
1062 |        "      <td>619</td>\n",
1063 |        "      <td>0</td>\n",
1064 |        "      <td>42</td>\n",
1065 |        "      <td>2</td>\n",
1066 |        "      <td>0.00</td>\n",
1067 |        "      <td>1</td>\n",
1068 |        "      <td>1</td>\n",
1069 |        "      <td>1</td>\n",
1070 |        "      <td>101348.88</td>\n",
1071 |        "      <td>1</td>\n",
1072 |        "      <td>1.0</td>\n",
1073 |        "      <td>0.0</td>\n",
1074 |        "      <td>0.0</td>\n",
1075 |        "    </tr>\n",
1076 |        "    <tr>\n",
1077 |        "      <th>1</th>\n",
1078 |        "      <td>608</td>\n",
1079 |        "      <td>0</td>\n",
1080 |        "      <td>41</td>\n",
1081 |        "      <td>1</td>\n",
1082 |        "      <td>83807.86</td>\n",
1083 |        "      <td>1</td>\n",
1084 |        "      <td>0</td>\n",
1085 |        "      <td>1</td>\n",
1086 |        "      <td>112542.58</td>\n",
1087 |        "      <td>0</td>\n",
1088 |        "      <td>0.0</td>\n",
1089 |        "      <td>0.0</td>\n",
1090 |        "      <td>1.0</td>\n",
1091 |        "    </tr>\n",
1092 |        "    <tr>\n",
1093 |        "      <th>2</th>\n",
1094 |        "      <td>502</td>\n",
1095 |        "      <td>0</td>\n",
1096 |        "      <td>42</td>\n",
1097 |        "      <td>8</td>\n",
1098 |        "      <td>159660.80</td>\n",
1099 |        "      <td>3</td>\n",
1100 |        "      <td>1</td>\n",
1101 |        "      <td>0</td>\n",
1102 |        "      <td>113931.57</td>\n",
1103 |        "      <td>1</td>\n",
1104 |        "      <td>1.0</td>\n",
1105 |        "      <td>0.0</td>\n",
1106 |        "      <td>0.0</td>\n",
1107 |        "    </tr>\n",
1108 |        "    <tr>\n",
1109 |        "      <th>3</th>\n",
1110 |        "      <td>699</td>\n",
1111 |        "      <td>0</td>\n",
1112 |        "      <td>39</td>\n",
1113 |        "      <td>1</td>\n",
1114 |        "      <td>0.00</td>\n",
1115 |        "      <td>2</td>\n",
1116 |        "      <td>0</td>\n",
1117 |        "      <td>0</td>\n",
1118 |        "      <td>93826.63</td>\n",
1119 |        "      <td>0</td>\n",
1120 |        "      <td>1.0</td>\n",
1121 |        "      <td>0.0</td>\n",
1122 |        "      <td>0.0</td>\n",
1123 |        "    </tr>\n",
1124 |        "    <tr>\n",
1125 |        "      <th>4</th>\n",
1126 |        "      <td>850</td>\n",
1127 |        "      <td>0</td>\n",
1128 |        "      <td>43</td>\n",
1129 |        "      <td>2</td>\n",
1130 |        "      <td>125510.82</td>\n",
1131 |        "      <td>1</td>\n",
1132 |        "      <td>1</td>\n",
1133 |        "      <td>1</td>\n",
1134 |        "      <td>79084.10</td>\n",
1135 |        "      <td>0</td>\n",
1136 |        "      <td>0.0</td>\n",
1137 |        "      <td>0.0</td>\n",
1138 |        "      <td>1.0</td>\n",
1139 |        "    </tr>\n",
1140 |        "  </tbody>\n",
1141 |        "</table>\n",
1142 |        "</div>"
1143 |       ],
1144 |       "text/plain": [
1145 |        "   CreditScore  Gender  Age  Tenure    Balance  NumOfProducts  HasCrCard  \\\n",
1146 |        "0          619       0   42       2       0.00              1          1   \n",
1147 |        "1          608       0   41       1   83807.86              1          0   \n",
1148 |        "2          502       0   42       8  159660.80              3          1   \n",
1149 |        "3          699       0   39       1       0.00              2          0   \n",
1150 |        "4          850       0   43       2  125510.82              1          1   \n",
1151 |        "\n",
1152 |        "   IsActiveMember  EstimatedSalary  Exited  Geography_France  \\\n",
1153 |        "0               1        101348.88       1               1.0   \n",
1154 |        "1               1        112542.58       0               0.0   \n",
1155 |        "2               0        113931.57       1               1.0   \n",
1156 |        "3               0         93826.63       0               1.0   \n",
1157 |        "4               1         79084.10       0               0.0   \n",
1158 |        "\n",
1159 |        "   Geography_Germany  Geography_Spain  \n",
1160 |        "0                0.0              0.0  \n",
1161 |        "1                0.0              1.0  \n",
1162 |        "2                0.0              0.0  \n",
1163 |        "3                0.0              0.0  \n",
1164 |        "4                0.0              1.0  "
1165 |       ]
1166 |      },
1167 |      "execution_count": 44,
1168 |      "metadata": {},
1169 |      "output_type": "execute_result"
1170 |     }
1171 |    ],
1172 |    "source": [
1173 |     "data.head()"
1174 |    ]
1175 |   },
1176 |   {
1177 |    "cell_type": "code",
1178 |    "execution_count": 45,
1179 |    "metadata": {},
1180 |    "outputs": [],
1181 |    "source": [
1182 |     "## DiVide the dataset into indepent and dependent features\n",
1183 |     "X=data.drop('Exited',axis=1)\n",
1184 |     "y=data['Exited']\n",
1185 |     "\n",
1186 |     "## Split the data in training and tetsing sets\n",
1187 |     "X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.2,random_state=42)\n",
1188 |     "\n",
1189 |     "## Scale these features\n",
1190 |     "scaler=StandardScaler()\n",
1191 |     "X_train=scaler.fit_transform(X_train)\n",
1192 |     "X_test=scaler.transform(X_test)\n"
1193 |    ]
1194 |   },
1195 |   {
1196 |    "cell_type": "code",
1197 |    "execution_count": 46,
1198 |    "metadata": {},
1199 |    "outputs": [
1200 |     {
1201 |      "data": {
1202 |       "text/plain": [
1203 |        "array([[ 0.35649971,  0.91324755, -0.6557859 , ...,  1.00150113,\n",
1204 |        "        -0.57946723, -0.57638802],\n",
1205 |        "       [-0.20389777,  0.91324755,  0.29493847, ..., -0.99850112,\n",
1206 |        "         1.72572313, -0.57638802],\n",
1207 |        "       [-0.96147213,  0.91324755, -1.41636539, ..., -0.99850112,\n",
1208 |        "        -0.57946723,  1.73494238],\n",
1209 |        "       ...,\n",
1210 |        "       [ 0.86500853, -1.09499335, -0.08535128, ...,  1.00150113,\n",
1211 |        "        -0.57946723, -0.57638802],\n",
1212 |        "       [ 0.15932282,  0.91324755,  0.3900109 , ...,  1.00150113,\n",
1213 |        "        -0.57946723, -0.57638802],\n",
1214 |        "       [ 0.47065475,  0.91324755,  1.15059039, ..., -0.99850112,\n",
1215 |        "         1.72572313, -0.57638802]])"
1216 |       ]
1217 |      },
1218 |      "execution_count": 46,
1219 |      "metadata": {},
1220 |      "output_type": "execute_result"
1221 |     }
1222 |    ],
1223 |    "source": [
1224 |     "X_train"
1225 |    ]
1226 |   },
1227 |   {
1228 |    "cell_type": "code",
1229 |    "execution_count": 47,
1230 |    "metadata": {},
1231 |    "outputs": [],
1232 |    "source": [
1233 |     "with open('scaler.pkl','wb') as file:\n",
1234 |     "    pickle.dump(scaler,file)"
1235 |    ]
1236 |   },
1237 |   {
1238 |    "cell_type": "code",
1239 |    "execution_count": 48,
1240 |    "metadata": {},
1241 |    "outputs": [
1242 |     {
1243 |      "data": {
1244 |       "text/html": [
1245 |        "<div>\n",
1246 |        "<style scoped>\n",
1247 |        "    .dataframe tbody tr th:only-of-type {\n",
1248 |        "        vertical-align: middle;\n",
1249 |        "    }\n",
1250 |        "\n",
1251 |        "    .dataframe tbody tr th {\n",
1252 |        "        vertical-align: top;\n",
1253 |        "    }\n",
1254 |        "\n",
1255 |        "    .dataframe thead th {\n",
1256 |        "        text-align: right;\n",
1257 |        "    }\n",
1258 |        "</style>\n",
1259 |        "<table border=\"1\" class=\"dataframe\">\n",
1260 |        "  <thead>\n",
1261 |        "    <tr style=\"text-align: right;\">\n",
1262 |        "      <th></th>\n",
1263 |        "      <th>CreditScore</th>\n",
1264 |        "      <th>Gender</th>\n",
1265 |        "      <th>Age</th>\n",
1266 |        "      <th>Tenure</th>\n",
1267 |        "      <th>Balance</th>\n",
1268 |        "      <th>NumOfProducts</th>\n",
1269 |        "      <th>HasCrCard</th>\n",
1270 |        "      <th>IsActiveMember</th>\n",
1271 |        "      <th>EstimatedSalary</th>\n",
1272 |        "      <th>Exited</th>\n",
1273 |        "      <th>Geography_France</th>\n",
1274 |        "      <th>Geography_Germany</th>\n",
1275 |        "      <th>Geography_Spain</th>\n",
1276 |        "    </tr>\n",
1277 |        "  </thead>\n",
1278 |        "  <tbody>\n",
1279 |        "    <tr>\n",
1280 |        "      <th>0</th>\n",
1281 |        "      <td>619</td>\n",
1282 |        "      <td>0</td>\n",
1283 |        "      <td>42</td>\n",
1284 |        "      <td>2</td>\n",
1285 |        "      <td>0.00</td>\n",
1286 |        "      <td>1</td>\n",
1287 |        "      <td>1</td>\n",
1288 |        "      <td>1</td>\n",
1289 |        "      <td>101348.88</td>\n",
1290 |        "      <td>1</td>\n",
1291 |        "      <td>1.0</td>\n",
1292 |        "      <td>0.0</td>\n",
1293 |        "      <td>0.0</td>\n",
1294 |        "    </tr>\n",
1295 |        "    <tr>\n",
1296 |        "      <th>1</th>\n",
1297 |        "      <td>608</td>\n",
1298 |        "      <td>0</td>\n",
1299 |        "      <td>41</td>\n",
1300 |        "      <td>1</td>\n",
1301 |        "      <td>83807.86</td>\n",
1302 |        "      <td>1</td>\n",
1303 |        "      <td>0</td>\n",
1304 |        "      <td>1</td>\n",
1305 |        "      <td>112542.58</td>\n",
1306 |        "      <td>0</td>\n",
1307 |        "      <td>0.0</td>\n",
1308 |        "      <td>0.0</td>\n",
1309 |        "      <td>1.0</td>\n",
1310 |        "    </tr>\n",
1311 |        "    <tr>\n",
1312 |        "      <th>2</th>\n",
1313 |        "      <td>502</td>\n",
1314 |        "      <td>0</td>\n",
1315 |        "      <td>42</td>\n",
1316 |        "      <td>8</td>\n",
1317 |        "      <td>159660.80</td>\n",
1318 |        "      <td>3</td>\n",
1319 |        "      <td>1</td>\n",
1320 |        "      <td>0</td>\n",
1321 |        "      <td>113931.57</td>\n",
1322 |        "      <td>1</td>\n",
1323 |        "      <td>1.0</td>\n",
1324 |        "      <td>0.0</td>\n",
1325 |        "      <td>0.0</td>\n",
1326 |        "    </tr>\n",
1327 |        "    <tr>\n",
1328 |        "      <th>3</th>\n",
1329 |        "      <td>699</td>\n",
1330 |        "      <td>0</td>\n",
1331 |        "      <td>39</td>\n",
1332 |        "      <td>1</td>\n",
1333 |        "      <td>0.00</td>\n",
1334 |        "      <td>2</td>\n",
1335 |        "      <td>0</td>\n",
1336 |        "      <td>0</td>\n",
1337 |        "      <td>93826.63</td>\n",
1338 |        "      <td>0</td>\n",
1339 |        "      <td>1.0</td>\n",
1340 |        "      <td>0.0</td>\n",
1341 |        "      <td>0.0</td>\n",
1342 |        "    </tr>\n",
1343 |        "    <tr>\n",
1344 |        "      <th>4</th>\n",
1345 |        "      <td>850</td>\n",
1346 |        "      <td>0</td>\n",
1347 |        "      <td>43</td>\n",
1348 |        "      <td>2</td>\n",
1349 |        "      <td>125510.82</td>\n",
1350 |        "      <td>1</td>\n",
1351 |        "      <td>1</td>\n",
1352 |        "      <td>1</td>\n",
1353 |        "      <td>79084.10</td>\n",
1354 |        "      <td>0</td>\n",
1355 |        "      <td>0.0</td>\n",
1356 |        "      <td>0.0</td>\n",
1357 |        "      <td>1.0</td>\n",
1358 |        "    </tr>\n",
1359 |        "    <tr>\n",
1360 |        "      <th>...</th>\n",
1361 |        "      <td>...</td>\n",
1362 |        "      <td>...</td>\n",
1363 |        "      <td>...</td>\n",
1364 |        "      <td>...</td>\n",
1365 |        "      <td>...</td>\n",
1366 |        "      <td>...</td>\n",
1367 |        "      <td>...</td>\n",
1368 |        "      <td>...</td>\n",
1369 |        "      <td>...</td>\n",
1370 |        "      <td>...</td>\n",
1371 |        "      <td>...</td>\n",
1372 |        "      <td>...</td>\n",
1373 |        "      <td>...</td>\n",
1374 |        "    </tr>\n",
1375 |        "    <tr>\n",
1376 |        "      <th>9995</th>\n",
1377 |        "      <td>771</td>\n",
1378 |        "      <td>1</td>\n",
1379 |        "      <td>39</td>\n",
1380 |        "      <td>5</td>\n",
1381 |        "      <td>0.00</td>\n",
1382 |        "      <td>2</td>\n",
1383 |        "      <td>1</td>\n",
1384 |        "      <td>0</td>\n",
1385 |        "      <td>96270.64</td>\n",
1386 |        "      <td>0</td>\n",
1387 |        "      <td>1.0</td>\n",
1388 |        "      <td>0.0</td>\n",
1389 |        "      <td>0.0</td>\n",
1390 |        "    </tr>\n",
1391 |        "    <tr>\n",
1392 |        "      <th>9996</th>\n",
1393 |        "      <td>516</td>\n",
1394 |        "      <td>1</td>\n",
1395 |        "      <td>35</td>\n",
1396 |        "      <td>10</td>\n",
1397 |        "      <td>57369.61</td>\n",
1398 |        "      <td>1</td>\n",
1399 |        "      <td>1</td>\n",
1400 |        "      <td>1</td>\n",
1401 |        "      <td>101699.77</td>\n",
1402 |        "      <td>0</td>\n",
1403 |        "      <td>1.0</td>\n",
1404 |        "      <td>0.0</td>\n",
1405 |        "      <td>0.0</td>\n",
1406 |        "    </tr>\n",
1407 |        "    <tr>\n",
1408 |        "      <th>9997</th>\n",
1409 |        "      <td>709</td>\n",
1410 |        "      <td>0</td>\n",
1411 |        "      <td>36</td>\n",
1412 |        "      <td>7</td>\n",
1413 |        "      <td>0.00</td>\n",
1414 |        "      <td>1</td>\n",
1415 |        "      <td>0</td>\n",
1416 |        "      <td>1</td>\n",
1417 |        "      <td>42085.58</td>\n",
1418 |        "      <td>1</td>\n",
1419 |        "      <td>1.0</td>\n",
1420 |        "      <td>0.0</td>\n",
1421 |        "      <td>0.0</td>\n",
1422 |        "    </tr>\n",
1423 |        "    <tr>\n",
1424 |        "      <th>9998</th>\n",
1425 |        "      <td>772</td>\n",
1426 |        "      <td>1</td>\n",
1427 |        "      <td>42</td>\n",
1428 |        "      <td>3</td>\n",
1429 |        "      <td>75075.31</td>\n",
1430 |        "      <td>2</td>\n",
1431 |        "      <td>1</td>\n",
1432 |        "      <td>0</td>\n",
1433 |        "      <td>92888.52</td>\n",
1434 |        "      <td>1</td>\n",
1435 |        "      <td>0.0</td>\n",
1436 |        "      <td>1.0</td>\n",
1437 |        "      <td>0.0</td>\n",
1438 |        "    </tr>\n",
1439 |        "    <tr>\n",
1440 |        "      <th>9999</th>\n",
1441 |        "      <td>792</td>\n",
1442 |        "      <td>0</td>\n",
1443 |        "      <td>28</td>\n",
1444 |        "      <td>4</td>\n",
1445 |        "      <td>130142.79</td>\n",
1446 |        "      <td>1</td>\n",
1447 |        "      <td>1</td>\n",
1448 |        "      <td>0</td>\n",
1449 |        "      <td>38190.78</td>\n",
1450 |        "      <td>0</td>\n",
1451 |        "      <td>1.0</td>\n",
1452 |        "      <td>0.0</td>\n",
1453 |        "      <td>0.0</td>\n",
1454 |        "    </tr>\n",
1455 |        "  </tbody>\n",
1456 |        "</table>\n",
1457 |        "<p>10000 rows × 13 columns</p>\n",
1458 |        "</div>"
1459 |       ],
1460 |       "text/plain": [
1461 |        "      CreditScore  Gender  Age  Tenure    Balance  NumOfProducts  HasCrCard  \\\n",
1462 |        "0             619       0   42       2       0.00              1          1   \n",
1463 |        "1             608       0   41       1   83807.86              1          0   \n",
1464 |        "2             502       0   42       8  159660.80              3          1   \n",
1465 |        "3             699       0   39       1       0.00              2          0   \n",
1466 |        "4             850       0   43       2  125510.82              1          1   \n",
1467 |        "...           ...     ...  ...     ...        ...            ...        ...   \n",
1468 |        "9995          771       1   39       5       0.00              2          1   \n",
1469 |        "9996          516       1   35      10   57369.61              1          1   \n",
1470 |        "9997          709       0   36       7       0.00              1          0   \n",
1471 |        "9998          772       1   42       3   75075.31              2          1   \n",
1472 |        "9999          792       0   28       4  130142.79              1          1   \n",
1473 |        "\n",
1474 |        "      IsActiveMember  EstimatedSalary  Exited  Geography_France  \\\n",
1475 |        "0                  1        101348.88       1               1.0   \n",
1476 |        "1                  1        112542.58       0               0.0   \n",
1477 |        "2                  0        113931.57       1               1.0   \n",
1478 |        "3                  0         93826.63       0               1.0   \n",
1479 |        "4                  1         79084.10       0               0.0   \n",
1480 |        "...              ...              ...     ...               ...   \n",
1481 |        "9995               0         96270.64       0               1.0   \n",
1482 |        "9996               1        101699.77       0               1.0   \n",
1483 |        "9997               1         42085.58       1               1.0   \n",
1484 |        "9998               0         92888.52       1               0.0   \n",
1485 |        "9999               0         38190.78       0               1.0   \n",
1486 |        "\n",
1487 |        "      Geography_Germany  Geography_Spain  \n",
1488 |        "0                   0.0              0.0  \n",
1489 |        "1                   0.0              1.0  \n",
1490 |        "2                   0.0              0.0  \n",
1491 |        "3                   0.0              0.0  \n",
1492 |        "4                   0.0              1.0  \n",
1493 |        "...                 ...              ...  \n",
1494 |        "9995                0.0              0.0  \n",
1495 |        "9996                0.0              0.0  \n",
1496 |        "9997                0.0              0.0  \n",
1497 |        "9998                1.0              0.0  \n",
1498 |        "9999                0.0              0.0  \n",
1499 |        "\n",
1500 |        "[10000 rows x 13 columns]"
1501 |       ]
1502 |      },
1503 |      "execution_count": 48,
1504 |      "metadata": {},
1505 |      "output_type": "execute_result"
1506 |     }
1507 |    ],
1508 |    "source": [
1509 |     "data"
1510 |    ]
1511 |   },
1512 |   {
1513 |    "cell_type": "markdown",
1514 |    "metadata": {},
1515 |    "source": [
1516 |     "### ANN Implementation"
1517 |    ]
1518 |   },
1519 |   {
1520 |    "cell_type": "code",
1521 |    "execution_count": 49,
1522 |    "metadata": {},
1523 |    "outputs": [],
1524 |    "source": [
1525 |     "import tensorflow as tf\n",
1526 |     "from tensorflow.keras.models import Sequential\n",
1527 |     "from tensorflow.keras.layers import Dense\n",
1528 |     "from tensorflow.keras.callbacks import EarlyStopping,TensorBoard\n",
1529 |     "import datetime"
1530 |    ]
1531 |   },
1532 |   {
1533 |    "cell_type": "code",
1534 |    "execution_count": 52,
1535 |    "metadata": {},
1536 |    "outputs": [
1537 |     {
1538 |      "data": {
1539 |       "text/plain": [
1540 |        "(12,)"
1541 |       ]
1542 |      },
1543 |      "execution_count": 52,
1544 |      "metadata": {},
1545 |      "output_type": "execute_result"
1546 |     }
1547 |    ],
1548 |    "source": [
1549 |     "(X_train.shape[1],)"
1550 |    ]
1551 |   },
1552 |   {
1553 |    "cell_type": "code",
1554 |    "execution_count": 54,
1555 |    "metadata": {},
1556 |    "outputs": [],
1557 |    "source": [
1558 |     "## Build Our ANN Model\n",
1559 |     "model=Sequential([\n",
1560 |     "    Dense(64,activation='relu',input_shape=(X_train.shape[1],)), ## HL1 Connected wwith input layer\n",
1561 |     "    Dense(32,activation='relu'), ## HL2\n",
1562 |     "    Dense(1,activation='sigmoid')  ## output layer\n",
1563 |     "]\n",
1564 |     "\n",
1565 |     ")"
1566 |    ]
1567 |   },
1568 |   {
1569 |    "cell_type": "code",
1570 |    "execution_count": 55,
1571 |    "metadata": {},
1572 |    "outputs": [
1573 |     {
1574 |      "name": "stdout",
1575 |      "output_type": "stream",
1576 |      "text": [
1577 |       "Model: \"sequential_1\"\n",
1578 |       "_________________________________________________________________\n",
1579 |       " Layer (type)                Output Shape              Param #   \n",
1580 |       "=================================================================\n",
1581 |       " dense_3 (Dense)             (None, 64)                832       \n",
1582 |       "                                                                 \n",
1583 |       " dense_4 (Dense)             (None, 32)                2080      \n",
1584 |       "                                                                 \n",
1585 |       " dense_5 (Dense)             (None, 1)                 33        \n",
1586 |       "                                                                 \n",
1587 |       "=================================================================\n",
1588 |       "Total params: 2945 (11.50 KB)\n",
1589 |       "Trainable params: 2945 (11.50 KB)\n",
1590 |       "Non-trainable params: 0 (0.00 Byte)\n",
1591 |       "_________________________________________________________________\n"
1592 |      ]
1593 |     }
1594 |    ],
1595 |    "source": [
1596 |     "model.summary()"
1597 |    ]
1598 |   },
1599 |   {
1600 |    "cell_type": "code",
1601 |    "execution_count": 58,
1602 |    "metadata": {},
1603 |    "outputs": [
1604 |     {
1605 |      "data": {
1606 |       "text/plain": [
1607 |        "<keras.src.losses.BinaryCrossentropy at 0x19a709a7710>"
1608 |       ]
1609 |      },
1610 |      "execution_count": 58,
1611 |      "metadata": {},
1612 |      "output_type": "execute_result"
1613 |     }
1614 |    ],
1615 |    "source": [
1616 |     "import tensorflow\n",
1617 |     "opt=tensorflow.keras.optimizers.Adam(learning_rate=0.01)\n",
1618 |     "loss=tensorflow.keras.losses.BinaryCrossentropy()\n",
1619 |     "loss"
1620 |    ]
1621 |   },
1622 |   {
1623 |    "cell_type": "code",
1624 |    "execution_count": 60,
1625 |    "metadata": {},
1626 |    "outputs": [],
1627 |    "source": [
1628 |     "## compile the model\n",
1629 |     "model.compile(optimizer=opt,loss=\"binary_crossentropy\",metrics=['accuracy'])"
1630 |    ]
1631 |   },
1632 |   {
1633 |    "cell_type": "code",
1634 |    "execution_count": 63,
1635 |    "metadata": {},
1636 |    "outputs": [],
1637 |    "source": [
1638 |     "## Set up the Tensorboard\n",
1639 |     "from tensorflow.keras.callbacks import EarlyStopping,TensorBoard\n",
1640 |     "\n",
1641 |     "log_dir=\"logs/fit/\" + datetime.datetime.now().strftime(\"%Y%m%d-%H%M%S\")\n",
1642 |     "tensorflow_callback=TensorBoard(log_dir=log_dir,histogram_freq=1)"
1643 |    ]
1644 |   },
1645 |   {
1646 |    "cell_type": "code",
1647 |    "execution_count": 66,
1648 |    "metadata": {},
1649 |    "outputs": [],
1650 |    "source": [
1651 |     "## Set up Early Stopping\n",
1652 |     "early_stopping_callback=EarlyStopping(monitor='val_loss',patience=10,restore_best_weights=True)\n"
1653 |    ]
1654 |   },
1655 |   {
1656 |    "cell_type": "code",
1657 |    "execution_count": 67,
1658 |    "metadata": {},
1659 |    "outputs": [
1660 |     {
1661 |      "name": "stdout",
1662 |      "output_type": "stream",
1663 |      "text": [
1664 |       "Epoch 1/100\n",
1665 |       "250/250 [==============================] - 1s 3ms/step - loss: 0.3496 - accuracy: 0.8591 - val_loss: 0.3424 - val_accuracy: 0.8595\n",
1666 |       "Epoch 2/100\n",
1667 |       "250/250 [==============================] - 1s 3ms/step - loss: 0.3426 - accuracy: 0.8622 - val_loss: 0.3427 - val_accuracy: 0.8580\n",
1668 |       "Epoch 3/100\n",
1669 |       "250/250 [==============================] - 1s 2ms/step - loss: 0.3397 - accuracy: 0.8630 - val_loss: 0.3508 - val_accuracy: 0.8605\n",
1670 |       "Epoch 4/100\n",
1671 |       "250/250 [==============================] - 1s 2ms/step - loss: 0.3406 - accuracy: 0.8622 - val_loss: 0.3583 - val_accuracy: 0.8590\n",
1672 |       "Epoch 5/100\n",
1673 |       "250/250 [==============================] - 1s 2ms/step - loss: 0.3378 - accuracy: 0.8626 - val_loss: 0.3424 - val_accuracy: 0.8555\n",
1674 |       "Epoch 6/100\n",
1675 |       "250/250 [==============================] - 1s 3ms/step - loss: 0.3322 - accuracy: 0.8654 - val_loss: 0.3423 - val_accuracy: 0.8600\n",
1676 |       "Epoch 7/100\n",
1677 |       "250/250 [==============================] - 1s 2ms/step - loss: 0.3311 - accuracy: 0.8661 - val_loss: 0.3412 - val_accuracy: 0.8605\n",
1678 |       "Epoch 8/100\n",
1679 |       "250/250 [==============================] - 1s 2ms/step - loss: 0.3256 - accuracy: 0.8665 - val_loss: 0.3515 - val_accuracy: 0.8540\n",
1680 |       "Epoch 9/100\n",
1681 |       "250/250 [==============================] - 1s 2ms/step - loss: 0.3280 - accuracy: 0.8665 - val_loss: 0.3623 - val_accuracy: 0.8540\n",
1682 |       "Epoch 10/100\n",
1683 |       "250/250 [==============================] - 1s 2ms/step - loss: 0.3251 - accuracy: 0.8677 - val_loss: 0.3474 - val_accuracy: 0.8540\n",
1684 |       "Epoch 11/100\n",
1685 |       "250/250 [==============================] - 1s 2ms/step - loss: 0.3227 - accuracy: 0.8690 - val_loss: 0.3571 - val_accuracy: 0.8565\n",
1686 |       "Epoch 12/100\n",
1687 |       "250/250 [==============================] - 1s 2ms/step - loss: 0.3216 - accuracy: 0.8664 - val_loss: 0.3565 - val_accuracy: 0.8550\n",
1688 |       "Epoch 13/100\n",
1689 |       "250/250 [==============================] - 1s 3ms/step - loss: 0.3171 - accuracy: 0.8699 - val_loss: 0.3569 - val_accuracy: 0.8570\n",
1690 |       "Epoch 14/100\n",
1691 |       "250/250 [==============================] - 1s 3ms/step - loss: 0.3141 - accuracy: 0.8689 - val_loss: 0.3562 - val_accuracy: 0.8605\n",
1692 |       "Epoch 15/100\n",
1693 |       "250/250 [==============================] - 1s 3ms/step - loss: 0.3141 - accuracy: 0.8704 - val_loss: 0.3554 - val_accuracy: 0.8600\n",
1694 |       "Epoch 16/100\n",
1695 |       "250/250 [==============================] - 1s 2ms/step - loss: 0.3115 - accuracy: 0.8733 - val_loss: 0.3585 - val_accuracy: 0.8555\n",
1696 |       "Epoch 17/100\n",
1697 |       "250/250 [==============================] - 1s 2ms/step - loss: 0.3081 - accuracy: 0.8725 - val_loss: 0.3636 - val_accuracy: 0.8535\n"
1698 |      ]
1699 |     }
1700 |    ],
1701 |    "source": [
1702 |     "### Train the model\n",
1703 |     "history=model.fit(\n",
1704 |     "    X_train,y_train,validation_data=(X_test,y_test),epochs=100,\n",
1705 |     "    callbacks=[tensorflow_callback,early_stopping_callback]\n",
1706 |     ")"
1707 |    ]
1708 |   },
1709 |   {
1710 |    "cell_type": "code",
1711 |    "execution_count": 68,
1712 |    "metadata": {},
1713 |    "outputs": [
1714 |     {
1715 |      "name": "stderr",
1716 |      "output_type": "stream",
1717 |      "text": [
1718 |       "e:\\UDemy Final\\ANN Classification\\venv\\Lib\\site-packages\\keras\\src\\engine\\training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.\n",
1719 |       "  saving_api.save_model(\n"
1720 |      ]
1721 |     }
1722 |    ],
1723 |    "source": [
1724 |     "model.save('model.h5')"
1725 |    ]
1726 |   },
1727 |   {
1728 |    "cell_type": "code",
1729 |    "execution_count": 70,
1730 |    "metadata": {},
1731 |    "outputs": [],
1732 |    "source": [
1733 |     "## Load Tensorboard Extension\n",
1734 |     "%load_ext tensorboard"
1735 |    ]
1736 |   },
1737 |   {
1738 |    "cell_type": "code",
1739 |    "execution_count": 75,
1740 |    "metadata": {},
1741 |    "outputs": [
1742 |     {
1743 |      "data": {
1744 |       "text/plain": [
1745 |        "Reusing TensorBoard on port 6014 (pid 16084), started 0:00:05 ago. (Use '!kill 16084' to kill it.)"
1746 |       ]
1747 |      },
1748 |      "metadata": {},
1749 |      "output_type": "display_data"
1750 |     },
1751 |     {
1752 |      "data": {
1753 |       "text/html": [
1754 |        "\n",
1755 |        "      <iframe id=\"tensorboard-frame-f580aa164607636e\" width=\"100%\" height=\"800\" frameborder=\"0\">\n",
1756 |        "      </iframe>\n",
1757 |        "      <script>\n",
1758 |        "        (function() {\n",
1759 |        "          const frame = document.getElementById(\"tensorboard-frame-f580aa164607636e\");\n",
1760 |        "          const url = new URL(\"http://localhost\");\n",
1761 |        "          const port = 6014;\n",
1762 |        "          if (port) {\n",
1763 |        "            url.port = port;\n",
1764 |        "          }\n",
1765 |        "          frame.src = url;\n",
1766 |        "        })();\n",
1767 |        "      </script>\n",
1768 |        "    "
1769 |       ],
1770 |       "text/plain": [
1771 |        "<IPython.core.display.HTML object>"
1772 |       ]
1773 |      },
1774 |      "metadata": {},
1775 |      "output_type": "display_data"
1776 |     }
1777 |    ],
1778 |    "source": [
1779 |     "%tensorboard --logdir logs/fit"
1780 |    ]
1781 |   },
1782 |   {
1783 |    "cell_type": "code",
1784 |    "execution_count": null,
1785 |    "metadata": {},
1786 |    "outputs": [],
1787 |    "source": [
1788 |     "### Load the pickle file\n"
1789 |    ]
1790 |   }
1791 |  ],
1792 |  "metadata": {
1793 |   "kernelspec": {
1794 |    "display_name": "Python 3",
1795 |    "language": "python",
1796 |    "name": "python3"
1797 |   },
1798 |   "language_info": {
1799 |    "codemirror_mode": {
1800 |     "name": "ipython",
1801 |     "version": 3
1802 |    },
1803 |    "file_extension": ".py",
1804 |    "mimetype": "text/x-python",
1805 |    "name": "python",
1806 |    "nbconvert_exporter": "python",
1807 |    "pygments_lexer": "ipython3",
1808 |    "version": "3.11.0"
1809 |   }
1810 |  },
1811 |  "nbformat": 4,
1812 |  "nbformat_minor": 2
1813 | }
1814 | 


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