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"source": [
"import pandas as pd\n",
"from sklearn.model_selection import train_test_split, cross_val_score\n",
"from sklearn.compose import ColumnTransformer\n",
"from sklearn.preprocessing import StandardScaler, OneHotEncoder\n",
"from sklearn.impute import SimpleImputer\n",
"from sklearn.decomposition import PCA\n",
"from sklearn.pipeline import Pipeline\n",
"from sklearn.linear_model import LogisticRegression\n"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "bd03e67e",
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"source": [
"df = pd.read_csv(\"/kaggle/input/credit-card-customer-churn-prediction/Churn_Modelling.csv\")"
]
},
{
"cell_type": "code",
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"source": [
"df = df.drop(columns=['RowNumber','CustomerId','Surname'])"
]
},
{
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"id": "82c42c57",
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>CreditScore</th>\n",
" <th>Geography</th>\n",
" <th>Gender</th>\n",
" <th>Age</th>\n",
" <th>Tenure</th>\n",
" <th>Balance</th>\n",
" <th>NumOfProducts</th>\n",
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" <td>1</td>\n",
" <td>1</td>\n",
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" <tr>\n",
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" <td>516</td>\n",
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"</div>"
],
"text/plain": [
" CreditScore Geography Gender Age Tenure Balance NumOfProducts \\\n",
"0 619 France Female 42 2 0.00 1 \n",
"1 608 Spain Female 41 1 83807.86 1 \n",
"2 502 France Female 42 8 159660.80 3 \n",
"3 699 France Female 39 1 0.00 2 \n",
"4 850 Spain Female 43 2 125510.82 1 \n",
"... ... ... ... ... ... ... ... \n",
"9995 771 France Male 39 5 0.00 2 \n",
"9996 516 France Male 35 10 57369.61 1 \n",
"9997 709 France Female 36 7 0.00 1 \n",
"9998 772 Germany Male 42 3 75075.31 2 \n",
"9999 792 France Female 28 4 130142.79 1 \n",
"\n",
" HasCrCard IsActiveMember EstimatedSalary Exited \n",
"0 1 1 101348.88 1 \n",
"1 0 1 112542.58 0 \n",
"2 1 0 113931.57 1 \n",
"3 0 0 93826.63 0 \n",
"4 1 1 79084.10 0 \n",
"... ... ... ... ... \n",
"9995 1 0 96270.64 0 \n",
"9996 1 1 101699.77 0 \n",
"9997 0 1 42085.58 1 \n",
"9998 1 0 92888.52 1 \n",
"9999 1 0 38190.78 0 \n",
"\n",
"[10000 rows x 11 columns]"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "5b922b0a",
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"tags": []
},
"outputs": [],
"source": [
"# define the input and output variables\n",
"X = df.drop(columns='Exited')\n",
"y = df['Exited']"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "96a5bd4b",
"metadata": {
"execution": {
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"status": "completed"
},
"tags": []
},
"outputs": [],
"source": [
"# split the data into training and test sets\n",
"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=2)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "a0f8527f",
"metadata": {
"execution": {
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"status": "completed"
},
"tags": []
},
"outputs": [],
"source": [
"from sklearn.preprocessing import OneHotEncoder"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "babb0c1c",
"metadata": {
"execution": {
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"status": "completed"
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"tags": []
},
"outputs": [],
"source": [
"# create a column transformer to standardize the numeric columns, one-hot encode the categorical columns, and impute missing values in all columns\n",
"num_cols = X_train.select_dtypes(include='number').columns.tolist()\n",
"# determine the categorical columns\n",
"cat_cols = X_train.select_dtypes(exclude='number').columns.tolist()\n",
"\n",
"# num_cols = ['numeric_col_1', 'numeric_col_2']\n",
"# cat_cols = ['cat_col_1', 'cat_col_2']\n",
"\n",
"transformer = ColumnTransformer(transformers=[\n",
" ('num', StandardScaler(), num_cols),\n",
" ('cat', OneHotEncoder(drop='first'), cat_cols),\n",
"])"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "3de68c6d",
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},
"tags": []
},
"outputs": [],
"source": [
"X_train = transformer.fit_transform(X_train)\n",
"X_test = transformer.fit_transform(X_test)"
]
},
{
"cell_type": "code",
"execution_count": 10,
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"outputs": [
{
"data": {
"text/plain": [
"array([[ 1.27754581, -1.23264342, -0.012743 , ..., 0. ,\n",
" 0. , 0. ],\n",
" [-0.49959477, 0.28410615, 0.33282985, ..., 1. ,\n",
" 0. , 0. ],\n",
" [ 0.67827747, -0.09508124, -1.39503438, ..., 0. ,\n",
" 0. , 0. ],\n",
" ...,\n",
" [ 2.06279398, -0.28467494, -0.70388869, ..., 0. ,\n",
" 0. , 1. ],\n",
" [-1.02653762, 1.42166833, -0.012743 , ..., 1. ,\n",
" 0. , 1. ],\n",
" [ 0.03768029, -1.04304972, 0.67840269, ..., 0. ,\n",
" 0. , 0. ]])"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"X_train"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "d547fcf0",
"metadata": {
"execution": {
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"status": "completed"
},
"tags": []
},
"outputs": [],
"source": [
"import tensorflow\n",
"from tensorflow import keras\n",
"from tensorflow.keras import Sequential\n",
"from tensorflow.keras.layers import Dense"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "d66f9057",
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},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"2023-01-05 14:35:42.698676: I tensorflow/core/common_runtime/process_util.cc:146] Creating new thread pool with default inter op setting: 2. Tune using inter_op_parallelism_threads for best performance.\n"
]
}
],
"source": [
"model = Sequential()\n",
"\n",
"model.add(Dense(3, activation='sigmoid',input_dim=11))\n",
"model.add(Dense(1, activation='sigmoid'))"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "5f4a0f17",
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},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Model: \"sequential\"\n",
"_________________________________________________________________\n",
"Layer (type) Output Shape Param # \n",
"=================================================================\n",
"dense (Dense) (None, 3) 36 \n",
"_________________________________________________________________\n",
"dense_1 (Dense) (None, 1) 4 \n",
"=================================================================\n",
"Total params: 40\n",
"Trainable params: 40\n",
"Non-trainable params: 0\n",
"_________________________________________________________________\n"
]
}
],
"source": [
"model.summary()"
]
},
{
"cell_type": "code",
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"id": "6c54d920",
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},
"outputs": [],
"source": [
"model.compile(loss='binary_crossentropy', optimizer='Adam', metrics=['accuracy'])"
]
},
{
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"tags": []
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"2023-01-05 14:35:42.962762: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:185] None of the MLIR Optimization Passes are enabled (registered 2)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch 1/100\n",
"200/200 [==============================] - 1s 3ms/step - loss: 0.5797 - accuracy: 0.7614 - val_loss: 0.5205 - val_accuracy: 0.8056\n",
"Epoch 2/100\n",
"200/200 [==============================] - 0s 2ms/step - loss: 0.5071 - accuracy: 0.7905 - val_loss: 0.4740 - val_accuracy: 0.8062\n",
"Epoch 3/100\n",
"200/200 [==============================] - 0s 2ms/step - loss: 0.4802 - accuracy: 0.7900 - val_loss: 0.4546 - val_accuracy: 0.8062\n",
"Epoch 4/100\n",
"200/200 [==============================] - 0s 2ms/step - loss: 0.4673 - accuracy: 0.7902 - val_loss: 0.4435 - val_accuracy: 0.8062\n",
"Epoch 5/100\n",
"200/200 [==============================] - 0s 2ms/step - loss: 0.4589 - accuracy: 0.7900 - val_loss: 0.4353 - val_accuracy: 0.8062\n",
"Epoch 6/100\n",
"200/200 [==============================] - 0s 2ms/step - loss: 0.4524 - accuracy: 0.7903 - val_loss: 0.4292 - val_accuracy: 0.8094\n",
"Epoch 7/100\n",
"200/200 [==============================] - 0s 2ms/step - loss: 0.4473 - accuracy: 0.7931 - val_loss: 0.4242 - val_accuracy: 0.8112\n",
"Epoch 8/100\n",
"200/200 [==============================] - 0s 2ms/step - loss: 0.4435 - accuracy: 0.7959 - val_loss: 0.4204 - val_accuracy: 0.8169\n",
"Epoch 9/100\n",
"200/200 [==============================] - 0s 2ms/step - loss: 0.4404 - accuracy: 0.7989 - val_loss: 0.4171 - val_accuracy: 0.8206\n",
"Epoch 10/100\n",
"200/200 [==============================] - 0s 2ms/step - loss: 0.4379 - accuracy: 0.7991 - val_loss: 0.4148 - val_accuracy: 0.8200\n",
"Epoch 11/100\n",
"200/200 [==============================] - 0s 2ms/step - loss: 0.4359 - accuracy: 0.8031 - val_loss: 0.4126 - val_accuracy: 0.8256\n",
"Epoch 12/100\n",
"200/200 [==============================] - 0s 2ms/step - loss: 0.4343 - accuracy: 0.8064 - val_loss: 0.4107 - val_accuracy: 0.8281\n",
"Epoch 13/100\n",
"200/200 [==============================] - 0s 2ms/step - loss: 0.4329 - accuracy: 0.8084 - val_loss: 0.4093 - val_accuracy: 0.8306\n",
"Epoch 14/100\n",
"200/200 [==============================] - 0s 2ms/step - loss: 0.4316 - accuracy: 0.8095 - val_loss: 0.4079 - val_accuracy: 0.8306\n",
"Epoch 15/100\n",
"200/200 [==============================] - 0s 2ms/step - loss: 0.4304 - accuracy: 0.8108 - val_loss: 0.4067 - val_accuracy: 0.8338\n",
"Epoch 16/100\n",
"200/200 [==============================] - 0s 2ms/step - loss: 0.4292 - accuracy: 0.8112 - val_loss: 0.4055 - val_accuracy: 0.8331\n",
"Epoch 17/100\n",
"200/200 [==============================] - 0s 2ms/step - loss: 0.4281 - accuracy: 0.8120 - val_loss: 0.4043 - val_accuracy: 0.8338\n",
"Epoch 18/100\n",
"200/200 [==============================] - 0s 2ms/step - loss: 0.4270 - accuracy: 0.8136 - val_loss: 0.4032 - val_accuracy: 0.8331\n",
"Epoch 19/100\n",
"200/200 [==============================] - 0s 2ms/step - loss: 0.4261 - accuracy: 0.8145 - val_loss: 0.4023 - val_accuracy: 0.8344\n",
"Epoch 20/100\n",
"200/200 [==============================] - 0s 2ms/step - loss: 0.4252 - accuracy: 0.8156 - val_loss: 0.4016 - val_accuracy: 0.8331\n",
"Epoch 21/100\n",
"200/200 [==============================] - 0s 2ms/step - loss: 0.4243 - accuracy: 0.8186 - val_loss: 0.4012 - val_accuracy: 0.8319\n",
"Epoch 22/100\n",
"200/200 [==============================] - 0s 2ms/step - loss: 0.4236 - accuracy: 0.8189 - val_loss: 0.4004 - val_accuracy: 0.8331\n",
"Epoch 23/100\n",
"200/200 [==============================] - 0s 2ms/step - loss: 0.4228 - accuracy: 0.8213 - val_loss: 0.3996 - val_accuracy: 0.8350\n",
"Epoch 24/100\n",
"200/200 [==============================] - 0s 2ms/step - loss: 0.4223 - accuracy: 0.8214 - val_loss: 0.3990 - val_accuracy: 0.8356\n",
"Epoch 25/100\n",
"200/200 [==============================] - 0s 2ms/step - loss: 0.4215 - accuracy: 0.8233 - val_loss: 0.3985 - val_accuracy: 0.8363\n",
"Epoch 26/100\n",
"200/200 [==============================] - 0s 2ms/step - loss: 0.4209 - accuracy: 0.8228 - val_loss: 0.3980 - val_accuracy: 0.8363\n",
"Epoch 27/100\n",
"200/200 [==============================] - 0s 2ms/step - loss: 0.4203 - accuracy: 0.8241 - val_loss: 0.3976 - val_accuracy: 0.8363\n",
"Epoch 28/100\n",
"200/200 [==============================] - 0s 2ms/step - loss: 0.4198 - accuracy: 0.8253 - val_loss: 0.3970 - val_accuracy: 0.8363\n",
"Epoch 29/100\n",
"200/200 [==============================] - 0s 2ms/step - loss: 0.4192 - accuracy: 0.8248 - val_loss: 0.3966 - val_accuracy: 0.8369\n",
"Epoch 30/100\n",
"200/200 [==============================] - 0s 2ms/step - loss: 0.4188 - accuracy: 0.8248 - val_loss: 0.3961 - val_accuracy: 0.8375\n",
"Epoch 31/100\n",
"200/200 [==============================] - 0s 2ms/step - loss: 0.4183 - accuracy: 0.8250 - val_loss: 0.3960 - val_accuracy: 0.8375\n",
"Epoch 32/100\n",
"200/200 [==============================] - 0s 2ms/step - loss: 0.4178 - accuracy: 0.8273 - val_loss: 0.3955 - val_accuracy: 0.8381\n",
"Epoch 33/100\n",
"200/200 [==============================] - 0s 2ms/step - loss: 0.4174 - accuracy: 0.8266 - val_loss: 0.3953 - val_accuracy: 0.8394\n",
"Epoch 34/100\n",
"200/200 [==============================] - 0s 2ms/step - loss: 0.4169 - accuracy: 0.8264 - val_loss: 0.3951 - val_accuracy: 0.8400\n",
"Epoch 35/100\n",
"200/200 [==============================] - 0s 2ms/step - loss: 0.4165 - accuracy: 0.8275 - val_loss: 0.3949 - val_accuracy: 0.8413\n",
"Epoch 36/100\n",
"200/200 [==============================] - 0s 2ms/step - loss: 0.4162 - accuracy: 0.8280 - val_loss: 0.3946 - val_accuracy: 0.8406\n",
"Epoch 37/100\n",
"200/200 [==============================] - 0s 2ms/step - loss: 0.4158 - accuracy: 0.8278 - val_loss: 0.3944 - val_accuracy: 0.8406\n",
"Epoch 38/100\n",
"200/200 [==============================] - 0s 2ms/step - loss: 0.4155 - accuracy: 0.8277 - val_loss: 0.3942 - val_accuracy: 0.8425\n",
"Epoch 39/100\n",
"200/200 [==============================] - 0s 2ms/step - loss: 0.4151 - accuracy: 0.8280 - val_loss: 0.3941 - val_accuracy: 0.8438\n",
"Epoch 40/100\n",
"200/200 [==============================] - 0s 2ms/step - loss: 0.4148 - accuracy: 0.8281 - val_loss: 0.3938 - val_accuracy: 0.8438\n",
"Epoch 41/100\n",
"200/200 [==============================] - 0s 2ms/step - loss: 0.4146 - accuracy: 0.8281 - val_loss: 0.3936 - val_accuracy: 0.8431\n",
"Epoch 42/100\n",
"200/200 [==============================] - 0s 2ms/step - loss: 0.4142 - accuracy: 0.8283 - val_loss: 0.3933 - val_accuracy: 0.8444\n",
"Epoch 43/100\n",
"200/200 [==============================] - 0s 2ms/step - loss: 0.4139 - accuracy: 0.8283 - val_loss: 0.3932 - val_accuracy: 0.8450\n",
"Epoch 44/100\n",
"200/200 [==============================] - 0s 2ms/step - loss: 0.4137 - accuracy: 0.8289 - val_loss: 0.3931 - val_accuracy: 0.8444\n",
"Epoch 45/100\n",
"200/200 [==============================] - 0s 2ms/step - loss: 0.4134 - accuracy: 0.8292 - val_loss: 0.3930 - val_accuracy: 0.8450\n",
"Epoch 46/100\n",
"200/200 [==============================] - 0s 2ms/step - loss: 0.4131 - accuracy: 0.8295 - val_loss: 0.3930 - val_accuracy: 0.8438\n",
"Epoch 47/100\n",
"200/200 [==============================] - 0s 2ms/step - loss: 0.4129 - accuracy: 0.8288 - val_loss: 0.3929 - val_accuracy: 0.8431\n",
"Epoch 48/100\n",
"200/200 [==============================] - 0s 2ms/step - loss: 0.4127 - accuracy: 0.8288 - val_loss: 0.3928 - val_accuracy: 0.8431\n",
"Epoch 49/100\n",
"200/200 [==============================] - 0s 2ms/step - loss: 0.4125 - accuracy: 0.8298 - val_loss: 0.3928 - val_accuracy: 0.8438\n",
"Epoch 50/100\n",
"200/200 [==============================] - 0s 2ms/step - loss: 0.4123 - accuracy: 0.8298 - val_loss: 0.3925 - val_accuracy: 0.8444\n",
"Epoch 51/100\n",
"200/200 [==============================] - 0s 2ms/step - loss: 0.4121 - accuracy: 0.8302 - val_loss: 0.3925 - val_accuracy: 0.8438\n",
"Epoch 52/100\n",
"200/200 [==============================] - 0s 2ms/step - loss: 0.4120 - accuracy: 0.8308 - val_loss: 0.3924 - val_accuracy: 0.8444\n",
"Epoch 53/100\n",
"200/200 [==============================] - 0s 2ms/step - loss: 0.4118 - accuracy: 0.8313 - val_loss: 0.3923 - val_accuracy: 0.8438\n",
"Epoch 54/100\n",
"200/200 [==============================] - 0s 2ms/step - loss: 0.4115 - accuracy: 0.8311 - val_loss: 0.3922 - val_accuracy: 0.8438\n",
"Epoch 55/100\n",
"200/200 [==============================] - 0s 2ms/step - loss: 0.4114 - accuracy: 0.8311 - val_loss: 0.3920 - val_accuracy: 0.8438\n",
"Epoch 56/100\n",
"200/200 [==============================] - 0s 2ms/step - loss: 0.4112 - accuracy: 0.8311 - val_loss: 0.3921 - val_accuracy: 0.8444\n",
"Epoch 57/100\n",
"200/200 [==============================] - 0s 2ms/step - loss: 0.4110 - accuracy: 0.8319 - val_loss: 0.3920 - val_accuracy: 0.8438\n",
"Epoch 58/100\n",
"200/200 [==============================] - 0s 2ms/step - loss: 0.4110 - accuracy: 0.8319 - val_loss: 0.3919 - val_accuracy: 0.8431\n",
"Epoch 59/100\n",
"200/200 [==============================] - 0s 2ms/step - loss: 0.4108 - accuracy: 0.8319 - val_loss: 0.3919 - val_accuracy: 0.8431\n",
"Epoch 60/100\n",
"200/200 [==============================] - 0s 2ms/step - loss: 0.4107 - accuracy: 0.8322 - val_loss: 0.3919 - val_accuracy: 0.8438\n",
"Epoch 61/100\n",
"200/200 [==============================] - 0s 2ms/step - loss: 0.4106 - accuracy: 0.8313 - val_loss: 0.3920 - val_accuracy: 0.8425\n",
"Epoch 62/100\n",
"200/200 [==============================] - 0s 2ms/step - loss: 0.4105 - accuracy: 0.8327 - val_loss: 0.3918 - val_accuracy: 0.8431\n",
"Epoch 63/100\n",
"200/200 [==============================] - 0s 2ms/step - loss: 0.4103 - accuracy: 0.8330 - val_loss: 0.3918 - val_accuracy: 0.8425\n",
"Epoch 64/100\n",
"200/200 [==============================] - 0s 2ms/step - loss: 0.4103 - accuracy: 0.8316 - val_loss: 0.3918 - val_accuracy: 0.8419\n",
"Epoch 65/100\n",
"200/200 [==============================] - 0s 2ms/step - loss: 0.4101 - accuracy: 0.8328 - val_loss: 0.3917 - val_accuracy: 0.8413\n",
"Epoch 66/100\n",
"200/200 [==============================] - 0s 2ms/step - loss: 0.4100 - accuracy: 0.8322 - val_loss: 0.3917 - val_accuracy: 0.8406\n",
"Epoch 67/100\n",
"200/200 [==============================] - 0s 2ms/step - loss: 0.4099 - accuracy: 0.8330 - val_loss: 0.3917 - val_accuracy: 0.8419\n",
"Epoch 68/100\n",
"200/200 [==============================] - 0s 2ms/step - loss: 0.4098 - accuracy: 0.8336 - val_loss: 0.3915 - val_accuracy: 0.8419\n",
"Epoch 69/100\n",
"200/200 [==============================] - 0s 2ms/step - loss: 0.4097 - accuracy: 0.8330 - val_loss: 0.3916 - val_accuracy: 0.8425\n",
"Epoch 70/100\n",
"200/200 [==============================] - 0s 2ms/step - loss: 0.4096 - accuracy: 0.8333 - val_loss: 0.3916 - val_accuracy: 0.8419\n",
"Epoch 71/100\n",
"200/200 [==============================] - 0s 2ms/step - loss: 0.4095 - accuracy: 0.8331 - val_loss: 0.3917 - val_accuracy: 0.8431\n",
"Epoch 72/100\n",
"200/200 [==============================] - 0s 2ms/step - loss: 0.4095 - accuracy: 0.8334 - val_loss: 0.3917 - val_accuracy: 0.8431\n",
"Epoch 73/100\n",
"200/200 [==============================] - 0s 2ms/step - loss: 0.4094 - accuracy: 0.8338 - val_loss: 0.3918 - val_accuracy: 0.8431\n",
"Epoch 74/100\n",
"200/200 [==============================] - 0s 2ms/step - loss: 0.4093 - accuracy: 0.8344 - val_loss: 0.3916 - val_accuracy: 0.8425\n",
"Epoch 75/100\n",
"200/200 [==============================] - 0s 2ms/step - loss: 0.4093 - accuracy: 0.8348 - val_loss: 0.3916 - val_accuracy: 0.8419\n",
"Epoch 76/100\n",
"200/200 [==============================] - 0s 2ms/step - loss: 0.4091 - accuracy: 0.8347 - val_loss: 0.3918 - val_accuracy: 0.8413\n",
"Epoch 77/100\n",
"200/200 [==============================] - 0s 2ms/step - loss: 0.4091 - accuracy: 0.8344 - val_loss: 0.3918 - val_accuracy: 0.8419\n",
"Epoch 78/100\n",
"200/200 [==============================] - 0s 2ms/step - loss: 0.4090 - accuracy: 0.8347 - val_loss: 0.3918 - val_accuracy: 0.8413\n",
"Epoch 79/100\n",
"200/200 [==============================] - 0s 2ms/step - loss: 0.4089 - accuracy: 0.8345 - val_loss: 0.3917 - val_accuracy: 0.8413\n",
"Epoch 80/100\n",
"200/200 [==============================] - 0s 2ms/step - loss: 0.4088 - accuracy: 0.8350 - val_loss: 0.3917 - val_accuracy: 0.8413\n",
"Epoch 81/100\n",
"200/200 [==============================] - 0s 2ms/step - loss: 0.4088 - accuracy: 0.8350 - val_loss: 0.3916 - val_accuracy: 0.8413\n",
"Epoch 82/100\n",
"200/200 [==============================] - 0s 2ms/step - loss: 0.4088 - accuracy: 0.8356 - val_loss: 0.3917 - val_accuracy: 0.8413\n",
"Epoch 83/100\n",
"200/200 [==============================] - 0s 2ms/step - loss: 0.4086 - accuracy: 0.8355 - val_loss: 0.3919 - val_accuracy: 0.8419\n",
"Epoch 84/100\n",
"200/200 [==============================] - 0s 2ms/step - loss: 0.4087 - accuracy: 0.8361 - val_loss: 0.3918 - val_accuracy: 0.8419\n",
"Epoch 85/100\n",
"200/200 [==============================] - 0s 2ms/step - loss: 0.4086 - accuracy: 0.8363 - val_loss: 0.3918 - val_accuracy: 0.8413\n",
"Epoch 86/100\n",
"200/200 [==============================] - 0s 2ms/step - loss: 0.4085 - accuracy: 0.8353 - val_loss: 0.3917 - val_accuracy: 0.8413\n",
"Epoch 87/100\n",
"200/200 [==============================] - 0s 2ms/step - loss: 0.4085 - accuracy: 0.8356 - val_loss: 0.3918 - val_accuracy: 0.8406\n",
"Epoch 88/100\n",
"200/200 [==============================] - 0s 2ms/step - loss: 0.4084 - accuracy: 0.8367 - val_loss: 0.3918 - val_accuracy: 0.8413\n",
"Epoch 89/100\n",
"200/200 [==============================] - 0s 2ms/step - loss: 0.4083 - accuracy: 0.8359 - val_loss: 0.3919 - val_accuracy: 0.8413\n",
"Epoch 90/100\n",
"200/200 [==============================] - 0s 2ms/step - loss: 0.4083 - accuracy: 0.8366 - val_loss: 0.3918 - val_accuracy: 0.8413\n",
"Epoch 91/100\n",
"200/200 [==============================] - 0s 2ms/step - loss: 0.4083 - accuracy: 0.8359 - val_loss: 0.3919 - val_accuracy: 0.8413\n",
"Epoch 92/100\n",
"200/200 [==============================] - 0s 2ms/step - loss: 0.4081 - accuracy: 0.8364 - val_loss: 0.3920 - val_accuracy: 0.8419\n",
"Epoch 93/100\n",
"200/200 [==============================] - 0s 2ms/step - loss: 0.4081 - accuracy: 0.8363 - val_loss: 0.3921 - val_accuracy: 0.8419\n",
"Epoch 94/100\n",
"200/200 [==============================] - 0s 2ms/step - loss: 0.4080 - accuracy: 0.8366 - val_loss: 0.3920 - val_accuracy: 0.8419\n",
"Epoch 95/100\n",
"200/200 [==============================] - 0s 2ms/step - loss: 0.4080 - accuracy: 0.8356 - val_loss: 0.3918 - val_accuracy: 0.8419\n",
"Epoch 96/100\n",
"200/200 [==============================] - 0s 2ms/step - loss: 0.4080 - accuracy: 0.8366 - val_loss: 0.3919 - val_accuracy: 0.8413\n",
"Epoch 97/100\n",
"200/200 [==============================] - 0s 2ms/step - loss: 0.4079 - accuracy: 0.8359 - val_loss: 0.3921 - val_accuracy: 0.8413\n",
"Epoch 98/100\n",
"200/200 [==============================] - 0s 2ms/step - loss: 0.4079 - accuracy: 0.8366 - val_loss: 0.3921 - val_accuracy: 0.8413\n",
"Epoch 99/100\n",
"200/200 [==============================] - 0s 2ms/step - loss: 0.4078 - accuracy: 0.8372 - val_loss: 0.3921 - val_accuracy: 0.8419\n",
"Epoch 100/100\n",
"200/200 [==============================] - 0s 2ms/step - loss: 0.4077 - accuracy: 0.8366 - val_loss: 0.3921 - val_accuracy: 0.8425\n"
]
}
],
"source": [
"history = model.fit(X_train,y_train, epochs=100, validation_split=0.2)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "841a48df",
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},
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},
"outputs": [
{
"data": {
"text/plain": [
"[array([[ 0.28448325, -0.02245507, -0.09248985],\n",
" [-2.7325892 , 0.16243261, 3.1476219 ],\n",
" [-0.03595207, 0.0515544 , -0.23199946],\n",
" [-0.33810443, -0.46231556, -0.42554903],\n",
" [-0.07120212, 0.25548536, -0.24803253],\n",
" [-0.03078385, 0.12762287, 0.06006212],\n",
" [-0.71293795, 2.3186512 , 1.2540545 ],\n",
" [ 0.13830076, -0.32949525, 0.0871588 ],\n",
" [-0.9696152 , -1.8935156 , 0.4912738 ],\n",
" [-0.11053856, -0.16680455, -0.18380535],\n",
" [ 0.6139277 , 1.3153068 , -0.26745653]], dtype=float32),\n",
" array([-0.02505367, -0.1938342 , -0.44920772], dtype=float32)]"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model.layers[0].get_weights()"
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "3fcf1fb5",
"metadata": {
"execution": {
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},
"tags": []
},
"outputs": [],
"source": [
"y_log = model.predict(X_test)"
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "eaa77573",
"metadata": {
"execution": {
"iopub.execute_input": "2023-01-05T14:36:19.956326Z",
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"end_time": "2023-01-05T14:36:19.964190",
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"start_time": "2023-01-05T14:36:19.902363",
"status": "completed"
},
"tags": []
},
"outputs": [],
"source": [
"import numpy as np\n",
"y_pred = np.where(y_log>.5,1,0)"
]
},
{
"cell_type": "code",
"execution_count": 19,
"id": "c2490165",
"metadata": {
"execution": {
"iopub.execute_input": "2023-01-05T14:36:20.067963Z",
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},
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"duration": 0.065211,
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"status": "completed"
},
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"0.836"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from sklearn.metrics import accuracy_score\n",
"\n",
"accuracy_score(y_test,y_pred)"
]
},
{
"cell_type": "code",
"execution_count": 20,
"id": "a52fec2b",
"metadata": {
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"import matplotlib.pyplot as plt\n",
"plt.plot(history.history['loss'])\n",
"plt.plot(history.history['val_loss'])"
]
},
{
"cell_type": "code",
"execution_count": 22,
"id": "1805f8ce",
"metadata": {
"execution": {
"iopub.execute_input": "2023-01-05T14:36:20.746818Z",
"iopub.status.busy": "2023-01-05T14:36:20.745928Z",
"iopub.status.idle": "2023-01-05T14:36:20.960009Z",
"shell.execute_reply": "2023-01-05T14:36:20.958754Z"
},
"papermill": {
"duration": 0.271047,
"end_time": "2023-01-05T14:36:20.962552",
"exception": false,
"start_time": "2023-01-05T14:36:20.691505",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x7efdf06bf510>]"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.plot(history.history['accuracy'])\n",
"plt.plot(history.history['val_accuracy'])"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "999d2a00",
"metadata": {
"papermill": {
"duration": 0.051822,
"end_time": "2023-01-05T14:36:21.066760",
"exception": false,
"start_time": "2023-01-05T14:36:21.014938",
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