dsnn/Assessment_S3

1{2 "cells": [3  {4   "cell_type": "markdown",5   "metadata": {6    "id": "PK-UE7d9aiQp"7   },8   "source": [9    "# **Telco Customer Churn** ( CRISP-DM Chapter 1)"10   ]11  },12  {13   "cell_type": "code",14   "execution_count": 2,15   "metadata": {16    "ExecuteTime": {17     "end_time": "2024-09-25T07:58:59.872738Z",18     "start_time": "2024-09-25T07:58:39.420662Z"19    }20   },21   "outputs": [],22   "source": [23    "import pandas as pd \n",24    "import numpy as np \n",25    "import matplotlib.pyplot as plt\n",26    "from sklearn.preprocessing import OneHotEncoder, MinMaxScaler, LabelEncoder\n",27    "from sklearn.linear_model import LogisticRegression\n",28    "from sklearn.model_selection import train_test_split\n",29    "from sklearn.metrics import ( accuracy_score,confusion_matrix,ConfusionMatrixDisplay)"30   ]31  },32  {33   "cell_type": "code",34   "execution_count": 3,35   "metadata": {36    "ExecuteTime": {37     "end_time": "2024-09-25T07:59:00.050813Z",38     "start_time": "2024-09-25T07:58:59.872738Z"39    },40    "id": "45DNTiSAqqbG"41   },42   "outputs": [],43   "source": [44    "df = pd.read_csv(\"D:\\p3\\Deifallah_Training\\WA_Fn-UseC_-Telco-Customer-Churn.csv\")  # Path to the CSV file"45   ]46  },47  {48   "cell_type": "code",49   "execution_count": 4,50   "metadata": {51    "ExecuteTime": {52     "end_time": "2024-09-25T07:59:00.065496Z",53     "start_time": "2024-09-25T07:59:00.050813Z"54    },55    "id": "U30UqclEq8AV"56   },57   "outputs": [],58   "source": [59    "import joblib\n",60    "\n",61    "# Columns for different preprocessing steps\n",62    "label_encode_cols = [\"Partner\", \"Dependents\", \"PhoneService\", \"PaperlessBilling\",'gender']\n",63    "one_hot_encode_cols = [\"MultipleLines\", \"InternetService\", \"OnlineSecurity\", \"OnlineBackup\",\n",64    "                       \"DeviceProtection\", \"TechSupport\", \"StreamingTV\", \"StreamingMovies\",\n",65    "                       \"Contract\", \"PaymentMethod\"]\n",66    "min_max_scale_cols = [\"tenure\", \"MonthlyCharges\", \"TotalCharges\"]"67   ]68  },69  {70   "cell_type": "code",71   "execution_count": 5,72   "metadata": {73    "ExecuteTime": {74     "end_time": "2024-09-25T07:59:00.216007Z",75     "start_time": "2024-09-25T07:59:00.066662Z"76    },77    "id": "7SPWJ6GKrOqY"78   },79   "outputs": [],80   "source": [81    "# Separate Features and Target\n",82    "# Drop 'customerID' and 'Churn' from the features, keeping 'Churn' as the target\n",83    "X = df.drop(columns=['customerID', 'Churn'])\n",84    "y = df['Churn']"85   ]86  },87  {88   "cell_type": "code",89   "execution_count": 6,90   "metadata": {91    "ExecuteTime": {92     "end_time": "2024-09-25T07:59:00.234131Z",93     "start_time": "2024-09-25T07:59:00.220934Z"94    },95    "colab": {96     "base_uri": "https://localhost:8080/"97    },98    "id": "LIvqF6zHrQ30",99    "outputId": "34013dde-2933-4bbb-850f-7ef9567c6746"100   },101   "outputs": [102    {103     "name": "stdout",104     "output_type": "stream",105     "text": [106      "Unique values in 'Churn' before encoding: ['No' 'Yes']\n"107     ]108    }109   ],110   "source": [111    "# Debug: Print unique values of target variable before encoding\n",112    "print(\"Unique values in 'Churn' before encoding:\", y.unique())"113   ]114  },115  {116   "cell_type": "code",117   "execution_count": 7,118   "metadata": {119    "ExecuteTime": {120     "end_time": "2024-09-25T07:59:00.246754Z",121     "start_time": "2024-09-25T07:59:00.238342Z"122    },123    "id": "7jeMpnz2rT28"124   },125   "outputs": [],126   "source": [127    "# Encode the Target Variable\n",128    "# Initialize the LabelEncoder\n",129    "le_target = LabelEncoder()\n",130    "\n",131    "# Fit and transform the target variable 'Churn' to numerical values\n",132    "y = le_target.fit_transform(y)"133   ]134  },135  {136   "cell_type": "code",137   "execution_count": 8,138   "metadata": {139    "ExecuteTime": {140     "end_time": "2024-09-25T07:59:00.261495Z",141     "start_time": "2024-09-25T07:59:00.248767Z"142    },143    "colab": {144     "base_uri": "https://localhost:8080/"145    },146    "id": "dh8FCbq_rV-1",147    "outputId": "7a3f7c63-237e-4554-80f6-357930290038"148   },149   "outputs": [150    {151     "name": "stdout",152     "output_type": "stream",153     "text": [154      "Unique values in 'Churn' after encoding: [0 0 1 ... 0 1 0]\n"155     ]156    }157   ],158   "source": [159    "# Debug: Print unique values of target variable after encoding\n",160    "print(\"Unique values in 'Churn' after encoding:\", y)"161   ]162  },163  {164   "cell_type": "markdown",165   "metadata": {166    "id": "DX1kDE5MlO_V"167   },168   "source": [169    "**Label Encoder for the Target Variable**"170   ]171  },172  {173   "cell_type": "code",174   "execution_count": 9,175   "metadata": {176    "ExecuteTime": {177     "end_time": "2024-09-25T07:59:00.275888Z",178     "start_time": "2024-09-25T07:59:00.266417Z"179    },180    "colab": {181     "base_uri": "https://localhost:8080/"182    },183    "id": "cak1q_TXlO6t",184    "outputId": "ca7a48fa-110c-47a3-9c66-9e6f5fe7a195"185   },186   "outputs": [187    {188     "name": "stdout",189     "output_type": "stream",190     "text": [191      "Label encoder for target saved.\n"192     ]193    }194   ],195   "source": [196    "# Save the LabelEncoder for target variable\n",197    "joblib.dump(le_target, 'label_encoder_target.pkl')\n",198    "print(\"Label encoder for target saved.\")"199   ]200  },201  {202   "cell_type": "markdown",203   "metadata": {204    "id": "Csdc_300smlH"205   },206   "source": [207    "**Handle Missing Values and Convert Data Types**"208   ]209  },210  {211   "cell_type": "code",212   "execution_count": 13,213   "metadata": {214    "ExecuteTime": {215     "end_time": "2024-09-25T07:59:45.397386Z",216     "start_time": "2024-09-25T07:59:45.370778Z"217    },218    "id": "USHIBBhOskhe"219   },220   "outputs": [],221   "source": [222    "# Replace non-numeric values with NaN and fill with the mean of the column\n",223    "X[min_max_scale_cols] = X[min_max_scale_cols].replace(' ', np.nan).astype(float)\n",224    "X[min_max_scale_cols] = X[min_max_scale_cols].fillna(X[min_max_scale_cols].mean())"225   ]226  },227  {228   "cell_type": "markdown",229   "metadata": {230    "id": "xz7XqOrclBxa"231   },232   "source": [233    "**Saving MinMax Scaler**"234   ]235  },236  {237   "cell_type": "code",238   "execution_count": 28,239   "metadata": {240    "ExecuteTime": {241     "end_time": "2024-09-25T06:48:44.847214Z",242     "start_time": "2024-09-25T06:48:44.839874Z"243    },244    "id": "0tKDcyW2sdPn"245   },246   "outputs": [],247   "source": [248    "# **Min-Max Scale Specified Columns**\n",249    "min_max_scaler = MinMaxScaler()\n",250    "scaled_numerical = min_max_scaler.fit_transform(X[min_max_scale_cols])"251   ]252  },253  {254   "cell_type": "code",255   "execution_count": 29,256   "metadata": {257    "ExecuteTime": {258     "end_time": "2024-09-25T06:48:45.346721Z",259     "start_time": "2024-09-25T06:48:45.341629Z"260    },261    "colab": {262     "base_uri": "https://localhost:8080/"263    },264    "id": "baIg8TAmxB-d",265    "outputId": "d73591d4-ee50-4e06-870d-b5ab696b1c58"266   },267   "outputs": [268    {269     "name": "stdout",270     "output_type": "stream",271     "text": [272      "Min-max scaler saved.\n"273     ]274    }275   ],276   "source": [277    "# Save the min-max scaler to a file\n",278    "joblib.dump(min_max_scaler, 'min_max_scaler.pkl')\n",279    "print(\"Min-max scaler saved.\")"280   ]281  },282  {283   "cell_type": "markdown",284   "metadata": {285    "id": "ByHKE8kGk8A_"286   },287   "source": [288    "**Saving Label Encoders**"289   ]290  },291  {292   "cell_type": "code",293   "execution_count": 30,294   "metadata": {295    "ExecuteTime": {296     "end_time": "2024-09-25T06:48:46.155089Z",297     "start_time": "2024-09-25T06:48:46.143342Z"298    },299    "id": "q3XjCdb3qyZx"300   },301   "outputs": [],302   "source": [303    "# **Label Encode Specified Columns**\n",304    "label_encoders = {}\n",305    "for col in label_encode_cols:\n",306    "    le = LabelEncoder()\n",307    "    X[col] = le.fit_transform(X[col])\n",308    "    label_encoders[col] = le"309   ]310  },311  {312   "cell_type": "code",313   "execution_count": 31,314   "metadata": {315    "ExecuteTime": {316     "end_time": "2024-09-25T06:48:46.617413Z",317     "start_time": "2024-09-25T06:48:46.612413Z"318    },319    "colab": {320     "base_uri": "https://localhost:8080/"321    },322    "id": "jbKiQRKksszV",323    "outputId": "2c18fcf6-79cb-4d5b-948e-5b3a6e44767b"324   },325   "outputs": [326    {327     "name": "stdout",328     "output_type": "stream",329     "text": [330      "Label encoders saved.\n"331     ]332    }333   ],334   "source": [335    "joblib.dump(label_encoders, 'label_encoders.pkl')\n",336    "print(\"Label encoders saved.\")"337   ]338  },339  {340   "cell_type": "markdown",341   "metadata": {342    "id": "kITWxNUok_tI"343   },344   "source": [345    "**Saving One-Hot Encoder**"346   ]347  },348  {349   "cell_type": "code",350   "execution_count": 32,351   "metadata": {352    "ExecuteTime": {353     "end_time": "2024-09-25T06:48:47.599921Z",354     "start_time": "2024-09-25T06:48:47.581012Z"355    },356    "colab": {357     "base_uri": "https://localhost:8080/"358    },359    "id": "Xu6uVwIfsdDq",360    "outputId": "454f1086-d1c5-4922-832a-2e5ddce9d4d1"361   },362   "outputs": [363    {364     "name": "stderr",365     "output_type": "stream",366     "text": [367      "D:\\Programs\\Anaconda\\Lib\\site-packages\\sklearn\\preprocessing\\_encoders.py:868: FutureWarning: `sparse` was renamed to `sparse_output` in version 1.2 and will be removed in 1.4. `sparse_output` is ignored unless you leave `sparse` to its default value.\n",368      "  warnings.warn(\n"369     ]370    }371   ],372   "source": [373    "# **One-Hot Encode Specified Columns**\n",374    "one_hot_encoder = OneHotEncoder(sparse_output=False, handle_unknown='ignore')\n",375    "one_hot_encoded = one_hot_encoder.fit_transform(X[one_hot_encode_cols])"376   ]377  },378  {379   "cell_type": "code",380   "execution_count": 33,381   "metadata": {382    "ExecuteTime": {383     "end_time": "2024-09-25T06:48:48.209920Z",384     "start_time": "2024-09-25T06:48:48.203709Z"385    },386    "colab": {387     "base_uri": "https://localhost:8080/"388    },389    "id": "-KUZ1O_4suvp",390    "outputId": "0aa8bc0e-9094-4968-f8de-bd0bc5c343e1"391   },392   "outputs": [393    {394     "name": "stdout",395     "output_type": "stream",396     "text": [397      "One-hot encoder saved.\n"398     ]399    }400   ],401   "source": [402    "# Save the one-hot encoder\n",403    "joblib.dump(one_hot_encoder, 'one_hot_encoder.pkl')\n",404    "print(\"One-hot encoder saved.\")"405   ]406  },407  {408   "cell_type": "markdown",409   "metadata": {410    "id": "8F7OQIAAsznK"411   },412   "source": [413    "**Combine Processed Columns**"414   ]415  },416  {417   "cell_type": "code",418   "execution_count": 34,419   "metadata": {420    "ExecuteTime": {421     "end_time": "2024-09-25T06:48:49.192927Z",422     "start_time": "2024-09-25T06:48:49.187519Z"423    },424    "id": "v3muz1l8s0Tt"425   },426   "outputs": [],427   "source": [428    "# Combine label encoded columns, scaled numerical columns, and one-hot encoded columns\n",429    "X_processed = np.hstack((X[label_encode_cols].values, scaled_numerical, one_hot_encoded))"430   ]431  },432  {433   "cell_type": "code",434   "execution_count": 35,435   "metadata": {436    "ExecuteTime": {437     "end_time": "2024-09-25T06:48:49.792738Z",438     "start_time": "2024-09-25T06:48:49.787178Z"439    },440    "colab": {441     "base_uri": "https://localhost:8080/"442    },443    "id": "0HcuGoCwiSnn",444    "outputId": "ca8fc2dc-a4ce-4cd2-dded-e5c2f7ce37e1",445    "scrolled": true446   },447   "outputs": [448    {449     "data": {450      "text/plain": [451       "array([[1., 0., 0., ..., 0., 1., 0.],\n",452       "       [0., 0., 1., ..., 0., 0., 1.],\n",453       "       [0., 0., 1., ..., 0., 0., 1.],\n",454       "       ...,\n",455       "       [1., 1., 0., ..., 0., 1., 0.],\n",456       "       [1., 0., 1., ..., 0., 0., 1.],\n",457       "       [0., 0., 1., ..., 0., 0., 0.]])"458      ]459     },460     "execution_count": 35,461     "metadata": {},462     "output_type": "execute_result"463    }464   ],465   "source": [466    "X_processed"467   ]468  },469  {470   "cell_type": "code",471   "execution_count": 36,472   "metadata": {473    "ExecuteTime": {474     "end_time": "2024-09-25T06:48:50.373368Z",475     "start_time": "2024-09-25T06:48:50.327521Z"476    },477    "id": "ThoZjINIs4vL"478   },479   "outputs": [],480   "source": [481    "# **Split Data into Training and Testing Sets**\n",482    "X_train, X_test, y_train, y_test = train_test_split(X_processed, y, test_size=0.2, random_state=42)"483   ]484  },485  {486   "cell_type": "markdown",487   "metadata": {488    "id": "H5ndA24FlHoF"489   },490   "source": [491    "**Saving the Trained Logistic Regression Model**\n"492   ]493  },494  {495   "cell_type": "code",496   "execution_count": 37,497   "metadata": {498    "ExecuteTime": {499     "end_time": "2024-09-25T06:48:57.210633Z",500     "start_time": "2024-09-25T06:48:56.846578Z"501    },502    "colab": {503     "base_uri": "https://localhost:8080/",504     "height": 74505    },506    "id": "Djk2YRSvsdee",507    "outputId": "0f76c552-d400-4bf7-ee1e-4b6a2c462d3f"508   },509   "outputs": [510    {511     "data": {512      "text/html": [513       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 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#d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression(max_iter=1000, random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression(max_iter=1000, random_state=42)</pre></div></div></div></div></div>"514      ],515      "text/plain": [516       "LogisticRegression(max_iter=1000, random_state=42)"517      ]518     },519     "execution_count": 37,520     "metadata": {},521     "output_type": "execute_result"522    }523   ],524   "source": [525    "# **Train the Model**\n",526    "model = LogisticRegression(max_iter=1000, random_state=42)\n",527    "model.fit(X_train, y_train)"528   ]529  },530  {531   "cell_type": "code",532   "execution_count": 38,533   "metadata": {534    "ExecuteTime": {535     "end_time": "2024-09-25T06:48:58.973360Z",536     "start_time": "2024-09-25T06:48:58.969170Z"537    }538   },539   "outputs": [],540   "source": [541    "y_pred_log = model.predict(X_test)"542   ]543  },544  {545   "cell_type": "code",546   "execution_count": 41,547   "metadata": {548    "ExecuteTime": {549     "end_time": "2024-09-25T06:49:33.209783Z",550     "start_time": "2024-09-25T06:49:32.054444Z"551    }552   },553   "outputs": [554    {555     "data": {556      "text/plain": [557       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x1f3201ff890>"558      ]559     },560     "execution_count": 41,561     "metadata": {},562     "output_type": "execute_result"563    },564    {565     "data": {566      "image/png": 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",567      "text/plain": [568       "<Figure size 640x480 with 2 Axes>"569      ]570     },571     "metadata": {},572     "output_type": "display_data"573    }574   ],575   "source": [576    "cf_matrix=confusion_matrix(y_test,y_pred=y_pred_log)\n",577    "display=ConfusionMatrixDisplay(cf_matrix, display_labels=model.classes_)\n",578    "display.plot()"579   ]580  },581  {582   "cell_type": "code",583   "execution_count": 42,584   "metadata": {585    "ExecuteTime": {586     "end_time": "2024-09-25T06:49:35.336682Z",587     "start_time": "2024-09-25T06:49:35.331053Z"588    },589    "colab": {590     "base_uri": "https://localhost:8080/"591    },592    "id": "QPG73cxOtA7q",593    "outputId": "de2ff6db-e1ef-4894-f722-58c9c21f321b"594   },595   "outputs": [596    {597     "name": "stdout",598     "output_type": "stream",599     "text": [600      "model saved as logistic_regression_model.pkl.\n"601     ]602    }603   ],604   "source": [605    "# Save the trained model\n",606    "model_file = 'logistic_regression_model.pkl'\n",607    "joblib.dump(model, model_file)\n",608    "print(f\"model saved as {model_file}.\")"609   ]610  },611  {612   "cell_type": "markdown",613   "metadata": {614    "id": "4Q--4FFDvEB_"615   },616   "source": [617    "\n",618    "###**requirements.txt**\n",619    "\n",620    "\n",621    "\n"622   ]623  },624  {625   "cell_type": "markdown",626   "metadata": {627    "id": "xm7lxCJFwgAS"628   },629   "source": [630    "*   pandas\n",631    "*   numpy==1.21.5\n",632    "*   scikit-learn==1.2.2\n",633    "*   gradio\n",634    "*   joblib"635   ]636  }637 ],638 "metadata": {639  "colab": {640   "collapsed_sections": [641    "X3f_6RRVitXl",642    "JLUWkWPdbToK",643    "zILILs5Ad8EL",644    "FGvQiyeDeH7e",645    "ojXJC5VSjLi5",646    "4Q--4FFDvEB_"647   ],648   "provenance": []649  },650  "hide_input": false,651  "kernelspec": {652   "display_name": "Python 3 (ipykernel)",653   "language": "python",654   "name": "python3"655  },656  "language_info": {657   "codemirror_mode": {658    "name": "ipython",659    "version": 3660   },661   "file_extension": ".py",662   "mimetype": "text/x-python",663   "name": "python",664   "nbconvert_exporter": "python",665   "pygments_lexer": "ipython3",666   "version": "3.11.7"667  }668 },669 "nbformat": 4,670 "nbformat_minor": 1671}672