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Customer-Churning-App

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slickdata commited on Jul 11, 2023

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f8130a1

1 Parent(s): c707469

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Files changed (5) hide show

CP_App.ipynb +681 -0
Telco-Customer-Churn.csv +0 -0
app.py +64 -0
best_model.joblib +3 -0
requirements.txt +437 -0

CP_App.ipynb ADDED Viewed

	@@ -0,0 +1,681 @@

+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "K1JbMys043S_",
+        "outputId": "c60da538-342e-4d5d-b678-0acdcf4f1976"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Mounted at /content/drive\n"
+          ]
+        }
+      ],
+      "source": [
+        "from google.colab import drive\n",
+        "drive.mount('/content/drive')"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "!pip install gradio"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "IbZYtX6k45IY",
+        "outputId": "c9221d56-1854-47ca-e1b9-41e4ee641859"
+      },
+      "execution_count": 2,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
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+            "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->gradio) (3.1.0)\n",
+            "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->gradio) (1.26.16)\n",
+            "Requirement already satisfied: anyio<5.0,>=3.0 in /usr/local/lib/python3.10/dist-packages (from httpcore<0.18.0,>=0.15.0->httpx->gradio) (3.7.0)\n",
+            "Requirement already satisfied: pyrsistent!=0.17.0,!=0.17.1,!=0.17.2,>=0.14.0 in /usr/local/lib/python3.10/dist-packages (from jsonschema>=3.0->altair>=4.2.0->gradio) (0.19.3)\n",
+            "Collecting uc-micro-py (from linkify-it-py<3,>=1->markdown-it-py[linkify]>=2.0.0->gradio)\n",
+            "  Downloading uc_micro_py-1.0.2-py3-none-any.whl (6.2 kB)\n",
+            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.1->pandas->gradio) (1.16.0)\n",
+            "Requirement already satisfied: exceptiongroup in /usr/local/lib/python3.10/dist-packages (from anyio<5.0,>=3.0->httpcore<0.18.0,>=0.15.0->httpx->gradio) (1.1.1)\n",
+            "Building wheels for collected packages: ffmpy\n",
+            "  Building wheel for ffmpy (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
+            "  Created wheel for ffmpy: filename=ffmpy-0.3.0-py3-none-any.whl size=4694 sha256=833a310a70972f4ea4972633c44808b65fc42559afcf441a716536e3ceddfb1f\n",
+            "  Stored in directory: /root/.cache/pip/wheels/0c/c2/0e/3b9c6845c6a4e35beb90910cc70d9ac9ab5d47402bd62af0df\n",
+            "Successfully built ffmpy\n",
+            "Installing collected packages: pydub, ffmpy, websockets, uc-micro-py, semantic-version, python-multipart, orjson, markdown-it-py, h11, aiofiles, uvicorn, starlette, mdit-py-plugins, linkify-it-py, huggingface-hub, httpcore, httpx, fastapi, gradio-client, gradio\n",
+            "  Attempting uninstall: markdown-it-py\n",
+            "    Found existing installation: markdown-it-py 3.0.0\n",
+            "    Uninstalling markdown-it-py-3.0.0:\n",
+            "      Successfully uninstalled markdown-it-py-3.0.0\n",
+            "Successfully installed aiofiles-23.1.0 fastapi-0.100.0 ffmpy-0.3.0 gradio-3.36.1 gradio-client-0.2.7 h11-0.14.0 httpcore-0.17.3 httpx-0.24.1 huggingface-hub-0.16.4 linkify-it-py-2.0.2 markdown-it-py-2.2.0 mdit-py-plugins-0.3.3 orjson-3.9.2 pydub-0.25.1 python-multipart-0.0.6 semantic-version-2.10.0 starlette-0.27.0 uc-micro-py-1.0.2 uvicorn-0.22.0 websockets-11.0.3\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import gradio as gr\n",
+        "import numpy as np\n",
+        "import pandas as pd\n",
+        "\n",
+        "from sklearn.pipeline import Pipeline\n",
+        "from sklearn.preprocessing import LabelEncoder, OneHotEncoder\n",
+        "from sklearn.ensemble import GradientBoostingClassifier\n",
+        "from sklearn.impute import SimpleImputer\n",
+        "from sklearn.compose import ColumnTransformer\n",
+        "from imblearn.over_sampling import ADASYN\n",
+        "from sklearn.preprocessing import StandardScaler\n",
+        "from sklearn.model_selection import train_test_split\n",
+        "from sklearn.model_selection import cross_val_score\n",
+        "from sklearn.metrics import confusion_matrix\n",
+        "from sklearn.metrics import classification_report\n",
+        "from sklearn.metrics import accuracy_score\n",
+        "from sklearn.metrics import roc_auc_score\n",
+        "from sklearn.metrics import roc_curve\n",
+        "from sklearn.metrics import precision_score, recall_score, f1_score\n",
+        "import sklearn.metrics as metrics\n",
+        "\n",
+        "from joblib import dump\n",
+        "import os\n",
+        "import warnings\n",
+        "warnings.filterwarnings('ignore')"
+      ],
+      "metadata": {
+        "id": "DuZ7ZW3E5Ge7"
+      },
+      "execution_count": 3,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "c_data = pd.read_csv('/content/drive/MyDrive/Colab Notebooks/Gradio/Telco-Customer-Churn.csv')\n",
+        "c_data.drop(['customerID','PhoneService','SeniorCitizen','StreamingMovies','StreamingTV'], axis=1, inplace=True)\n",
+        "c_data.info()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "nDm6h1Pl6tRd",
+        "outputId": "14740557-c7d4-40fa-9efb-e9882d6c42ed"
+      },
+      "execution_count": 4,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "<class 'pandas.core.frame.DataFrame'>\n",
+            "RangeIndex: 7043 entries, 0 to 7042\n",
+            "Data columns (total 16 columns):\n",
+            " #   Column            Non-Null Count  Dtype  \n",
+            "---  ------            --------------  -----  \n",
+            " 0   gender            7043 non-null   object \n",
+            " 1   Partner           7043 non-null   object \n",
+            " 2   Dependents        7043 non-null   object \n",
+            " 3   tenure            7043 non-null   int64  \n",
+            " 4   MultipleLines     7043 non-null   object \n",
+            " 5   InternetService   7043 non-null   object \n",
+            " 6   OnlineSecurity    7043 non-null   object \n",
+            " 7   OnlineBackup      7043 non-null   object \n",
+            " 8   DeviceProtection  7043 non-null   object \n",
+            " 9   TechSupport       7043 non-null   object \n",
+            " 10  Contract          7043 non-null   object \n",
+            " 11  PaperlessBilling  7043 non-null   object \n",
+            " 12  PaymentMethod     7043 non-null   object \n",
+            " 13  MonthlyCharges    7043 non-null   float64\n",
+            " 14  TotalCharges      7043 non-null   object \n",
+            " 15  Churn             7043 non-null   object \n",
+            "dtypes: float64(1), int64(1), object(14)\n",
+            "memory usage: 880.5+ KB\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "c_data['TotalCharges'] = pd.to_numeric(c_data['TotalCharges'], errors='coerce')\n",
+        "c_data.info()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "w_SwTyvi5GYI",
+        "outputId": "4e29588c-f81a-4d1e-d626-8a80b3f02ce9"
+      },
+      "execution_count": 5,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "<class 'pandas.core.frame.DataFrame'>\n",
+            "RangeIndex: 7043 entries, 0 to 7042\n",
+            "Data columns (total 16 columns):\n",
+            " #   Column            Non-Null Count  Dtype  \n",
+            "---  ------            --------------  -----  \n",
+            " 0   gender            7043 non-null   object \n",
+            " 1   Partner           7043 non-null   object \n",
+            " 2   Dependents        7043 non-null   object \n",
+            " 3   tenure            7043 non-null   int64  \n",
+            " 4   MultipleLines     7043 non-null   object \n",
+            " 5   InternetService   7043 non-null   object \n",
+            " 6   OnlineSecurity    7043 non-null   object \n",
+            " 7   OnlineBackup      7043 non-null   object \n",
+            " 8   DeviceProtection  7043 non-null   object \n",
+            " 9   TechSupport       7043 non-null   object \n",
+            " 10  Contract          7043 non-null   object \n",
+            " 11  PaperlessBilling  7043 non-null   object \n",
+            " 12  PaymentMethod     7043 non-null   object \n",
+            " 13  MonthlyCharges    7043 non-null   float64\n",
+            " 14  TotalCharges      7032 non-null   float64\n",
+            " 15  Churn             7043 non-null   object \n",
+            "dtypes: float64(2), int64(1), object(13)\n",
+            "memory usage: 880.5+ KB\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Removing missing values\n",
+        "c_data.dropna(inplace=True)\n",
+        "c_data.duplicated().sum()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "AuIXmyf_5GEm",
+        "outputId": "dbe796ae-7870-4cde-c81d-d1abb3da6382"
+      },
+      "execution_count": 6,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "26"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 6
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#le = LabelEncoder()\n",
+        "#c_data['Churn'] = le.fit_transform(c_data['Churn'])\n",
+        "#c_data.head()"
+      ],
+      "metadata": {
+        "id": "EUI4hmnA8rcL"
+      },
+      "execution_count": 7,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "for col in c_data.columns:\n",
+        "    print(f\"Column '{col}' categories: {c_data[col].unique()}\")"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "2cyG_RbM_yX_",
+        "outputId": "558cfe16-f354-4681-b973-a9809d7bc5e8"
+      },
+      "execution_count": 8,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Column 'gender' categories: ['Female' 'Male']\n",
+            "Column 'Partner' categories: ['Yes' 'No']\n",
+            "Column 'Dependents' categories: ['No' 'Yes']\n",
+            "Column 'tenure' categories: [ 1 34  2 45  8 22 10 28 62 13 16 58 49 25 69 52 71 21 12 30 47 72 17 27\n",
+            "  5 46 11 70 63 43 15 60 18 66  9  3 31 50 64 56  7 42 35 48 29 65 38 68\n",
+            " 32 55 37 36 41  6  4 33 67 23 57 61 14 20 53 40 59 24 44 19 54 51 26 39]\n",
+            "Column 'MultipleLines' categories: ['No phone service' 'No' 'Yes']\n",
+            "Column 'InternetService' categories: ['DSL' 'Fiber optic' 'No']\n",
+            "Column 'OnlineSecurity' categories: ['No' 'Yes' 'No internet service']\n",
+            "Column 'OnlineBackup' categories: ['Yes' 'No' 'No internet service']\n",
+            "Column 'DeviceProtection' categories: ['No' 'Yes' 'No internet service']\n",
+            "Column 'TechSupport' categories: ['No' 'Yes' 'No internet service']\n",
+            "Column 'Contract' categories: ['Month-to-month' 'One year' 'Two year']\n",
+            "Column 'PaperlessBilling' categories: ['Yes' 'No']\n",
+            "Column 'PaymentMethod' categories: ['Electronic check' 'Mailed check' 'Bank transfer (automatic)'\n",
+            " 'Credit card (automatic)']\n",
+            "Column 'MonthlyCharges' categories: [29.85 56.95 53.85 ... 63.1  44.2  78.7 ]\n",
+            "Column 'TotalCharges' categories: [  29.85 1889.5   108.15 ...  346.45  306.6  6844.5 ]\n",
+            "Column 'Churn' categories: ['No' 'Yes']\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "y = c_data['Churn']                         # Target Variable\n",
+        "X = c_data.drop('Churn', axis =1)          # Independent Variable"
+      ],
+      "metadata": {
+        "id": "7pL-2l8T8rU3"
+      },
+      "execution_count": 9,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "numeric_transformer = Pipeline(steps = [('imputer',SimpleImputer(strategy = 'mean')),('scaler',StandardScaler())])\n",
+        "categorical_transformer = Pipeline(steps = [('imputer',SimpleImputer(strategy = 'most_frequent')),('one_hot_encoder',OneHotEncoder(handle_unknown='ignore', categories='auto', sparse=False))])"
+      ],
+      "metadata": {
+        "id": "HWA2tbQBJU2e"
+      },
+      "execution_count": 10,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "data_numeric =['tenure','MonthlyCharges','TotalCharges']\n",
+        "data_categorical =['gender','Partner','Dependents','MultipleLines','InternetService','OnlineSecurity','OnlineBackup','DeviceProtection','TechSupport','Contract','PaperlessBilling','PaymentMethod']\n",
+        "preprocessor =ColumnTransformer(transformers =[('numeric',numeric_transformer,data_numeric),('categoric',categorical_transformer,data_categorical)])"
+      ],
+      "metadata": {
+        "id": "ObJQIJ8RJxXl"
+      },
+      "execution_count": 11,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Identify numeric and non-numeric columns\n",
+        "#num_cols = X.select_dtypes(include=[np.number]).columns.tolist()\n",
+        "#cat_cols = X.select_dtypes(exclude=[np.number]).columns.tolist()\n",
+        "\n",
+        "#X_cat = X[cat_cols].copy()\n",
+        "#X_num = X[num_cols].copy()\n"
+      ],
+      "metadata": {
+        "id": "7T37-Hq78q95"
+      },
+      "execution_count": 12,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Creating imputer variables\n",
+        "#numerical_imputer = SimpleImputer(strategy = \"mean\")\n",
+        "#categorical_imputer = SimpleImputer(strategy = \"most_frequent\")\n",
+        "\n",
+        "\n",
+        "\n",
+        "# Define the column transformer\n",
+        "#categorical_features = cat_cols\n",
+        "#categorical_transformer = Pipeline(steps=[('ohc', OneHotEncoder(handle_unknown='ignore', categories='auto', sparse=False))])\n",
+        "#preprocessor = ColumnTransformer(transformers=[('cat', categorical_transformer, cat_cols)])\n"
+      ],
+      "metadata": {
+        "id": "Hx-SIs2K8q2M"
+      },
+      "execution_count": 13,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Fitting the Imputer\n",
+        "#X_cat_imputed = categorical_imputer.fit_transform(X_cat)\n",
+        "#X_num_imputed = numerical_imputer.fit_transform(X_num)"
+      ],
+      "metadata": {
+        "id": "LhfvzFX-KnCe"
+      },
+      "execution_count": 14,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#ohe = OneHotEncoder(handle_unknown='ignore')\n",
+        "#X_cat_encoded = ohe.fit(X_cat_imputed)\n",
+        "#X_cat_encoded = pd.DataFrame(ohe.transform(X_cat_imputed).toarray(),\n",
+        "#                                   columns=ohe.get_feature_names_out(cat_cols))\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "6N8dMlRuKm7k"
+      },
+      "execution_count": 15,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#scaler = StandardScaler()\n",
+        "#X_num_scaled = scaler.fit_transform(X_num_imputed)\n",
+        "#X_num_sc = pd.DataFrame(X_num_scaled, columns = num_cols)\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "EtrHYrNcOvHM"
+      },
+      "execution_count": 16,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#X_df = pd.concat([X_num_sc,X_cat_encoded], axis =1)"
+      ],
+      "metadata": {
+        "id": "aATpxlXiTA83"
+      },
+      "execution_count": 17,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=30)"
+      ],
+      "metadata": {
+        "id": "cxIt0C7TZ91X"
+      },
+      "execution_count": 18,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "gbc = GradientBoostingClassifier(learning_rate = 0.1, max_depth = 5, n_estimators=50, random_state=30)\n",
+        "gbc = Pipeline(steps =[('processor',preprocessor),('estimator',gbc)])\n",
+        "model = gbc.fit(X_train,y_train)\n",
+        "\n",
+        "# make predictions on the test data\n",
+        "y_pred = model.predict(X_test)\n",
+        "\n",
+        "# generate the classification report\n",
+        "report = classification_report(y_test, y_pred)\n",
+        "name = 'Gradient Boosting Classifier'\n",
+        "# print the classification report\n",
+        "print(f'{name} classification report:\\n{report}\\n')\n"
+      ],
+      "metadata": {
+        "id": "DRT-nIuvOu9y",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "ca6a84f9-cf35-4bdb-aaa4-c0155128580c"
+      },
+      "execution_count": 19,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Gradient Boosting Classifier classification report:\n",
+            "              precision    recall  f1-score   support\n",
+            "\n",
+            "          No       0.83      0.89      0.86      1031\n",
+            "         Yes       0.64      0.51      0.57       376\n",
+            "\n",
+            "    accuracy                           0.79      1407\n",
+            "   macro avg       0.74      0.70      0.72      1407\n",
+            "weighted avg       0.78      0.79      0.78      1407\n",
+            "\n",
+            "\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "  # create a dictionary of a model to fit\n",
+        "  #models = {'Gradient Boosting Classifier': GradientBoostingClassifier(learning_rate = 0.1, max_depth = 5, n_estimators=50, random_state=30),}\n",
+        "  # iterate over the models and fit each one to the resampled training data\n",
+        "  #for name, model in models.items():\n",
+        "  #  model.fit(X_train, y_train)\n",
+        "\n",
+        "  # evaluate each model using cross-validation based on ROC-AUC\n",
+        "  #roc_auc_scores = {}\n",
+        "  #for name, model in models.items():\n",
+        "  #  scores = cross_val_score(model, X_train, y_train, cv=5, scoring='roc_auc')\n",
+        "  #  roc_auc_scores[name] = scores.mean()\n",
+        "\n",
+        "  # print the ROC-AUC scores for each model\n",
+        "  #for name, score in roc_auc_scores.items():\n",
+        "  #  print(f'{name}: {score}')\n",
+        "\n",
+        "  # choose the model with the highest ROC-AUC score\n",
+        "  #best_model_name = max(roc_auc_scores, key=roc_auc_scores.get)\n",
+        "  #best_model = models[best_model_name]\n",
+        "  #print(f'Best model: {best_model_name}')\n",
+        "\n",
+        "\n",
+        "  # iterate over the models and make predictions on the test data for each one\n",
+        "  #for name, model in models.items():\n",
+        "    # fit the model to the resampled training data\n",
+        "  #  model.fit(X_train, y_train)\n",
+        "    # make predictions on the test data\n",
+        "  #  y_pred = model.predict(X_test)\n",
+        "    # generate the classification report\n",
+        "  #  report = classification_report(y_test, y_pred)\n",
+        "    # print the classification report\n",
+        " #   print(f'{name} classification report:\\n{report}\\n')\n",
+        "\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "XW_eIe9ROu2s"
+      },
+      "execution_count": 20,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# set the destination path to the \"export\" directory\n",
+        "destination = \".\"\n",
+        "\n",
+        "#best_model = gbc\n",
+        "models = {\"best_model\": gbc}\n",
+        "\n",
+        "# loop through the models and save them using joblib.dump()\n",
+        "for name, model in models.items():\n",
+        "    dump(model, os.path.join(destination, f\"{name}.joblib\"))\n"
+      ],
+      "metadata": {
+        "id": "cVzILcaoOusD"
+      },
+      "execution_count": 21,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "!pip freeze > requirements.txt"
+      ],
+      "metadata": {
+        "id": "HrsFQmUROufW"
+      },
+      "execution_count": 22,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#for name, model in models.items():\n",
+        "#    dump(model, os.path.join(destination, f\"{name}.joblib\"))\n",
+        "#    if os.path.exists(os.path.join(destination, f\"{name}.joblib\")):\n",
+        "#        print(f\"{name} saved successfully!\")\n",
+        "#    else:\n",
+        "#        print(f\"{name} failed to save.\")"
+      ],
+      "metadata": {
+        "id": "xsrbuX4JKmnR"
+      },
+      "execution_count": 23,
+      "outputs": []
+    }
+  ]
+}

Telco-Customer-Churn.csv ADDED Viewed

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app.py ADDED Viewed

	@@ -0,0 +1,64 @@

+import gradio as gr
+import pickle
+import pandas as pd
+import numpy as np
+import joblib
+from gradio.components import *
+#define prediction function
+def make_prediction(gender, Partner, Dependents, tenure, MultipleLines,
+       InternetService, OnlineSecurity, OnlineBackup, DeviceProtection,
+       TechSupport, Contract, PaperlessBilling, PaymentMethod,
+       MonthlyCharges, TotalCharges):
+    #make a dataframe from input data
+    input_data = pd.DataFrame({'gender':[gender], 'Partner':[Partner], 'Dependents':[Dependents], 'tenure':[tenure], 'MultipleLines':[MultipleLines],
+       'InternetService':[InternetService], 'OnlineSecurity':[OnlineSecurity], 'OnlineBackup':[OnlineBackup], 'DeviceProtection':[DeviceProtection],
+       'TechSupport':[TechSupport], 'Contract':[Contract], 'PaperlessBilling':[PaperlessBilling], 'PaymentMethod':[PaymentMethod],
+       'MonthlyCharges':[MonthlyCharges], 'TotalCharges':[TotalCharges]})
+   #load already saved pipeline and make predictions
+    with open("best_model.joblib", "rb") as f:
+        model = joblib.load(f)
+        predt = model.predict(input_data)
+    #return prediction
+    if predt == 'Yes':
+        return 'Customer Will Churn'
+    return 'Customer Will Not Churn'
+#create the input components for gradio
+gender_input = gr.Dropdown(choices =['Female', 'Male'])
+Partner_input = gr.Dropdown(choices =['Yes', 'No'])
+Dependents_input = gr.Dropdown(choices =['Yes', 'No'])
+tenure_input = gr.Number()
+MultipleLines_input = gr.Dropdown(choices =['No phone service', 'No', 'Yes'])
+InternetService_input = gr.Dropdown(choices =['DSL', 'Fiber optic', 'No'])
+OnlineSecurity_input = gr.Dropdown(choices =['No', 'Yes', 'No internet service'])
+OnlineBackup_input = gr.Dropdown(choices =['Yes', 'No', 'No internet service'])
+DeviceProtection_input = gr.Dropdown(choices =['No', 'Yes', 'No internet service'])
+TechSupport_input = gr.Dropdown(choices =['No', 'Yes', 'No internet service'])
+Contract_input = gr.Dropdown(choices =['Month-to-month', 'One year', 'Two year'])
+PaperlessBilling_input = gr.Dropdown(choices =['Yes', 'No'])
+PaymentMethod_input = gr.Dropdown(choices =['Electronic check', 'Mailed check', 'Bank transfer (automatic)','Credit card (automatic)'])
+MonthlyCharges_input = gr.Number()
+TotalCharges_input = gr.Number()
+output = gr.Textbox(label='Prediction')
+app = gr.Interface(fn =make_prediction, inputs =[gender_input,
+                                                 Partner_input,
+                                                 Dependents_input,
+                                                 tenure_input,
+                                                 MultipleLines_input,
+                                                 InternetService_input,
+                                                 OnlineSecurity_input,
+                                                 OnlineBackup_input,
+                                                 DeviceProtection_input,
+                                                 TechSupport_input,
+                                                 Contract_input,
+                                                 PaperlessBilling_input,
+                                                 PaymentMethod_input,
+                                                 MonthlyCharges_input,
+                                                 TotalCharges_input], outputs = output)
+app.launch(share = True, debug = True)

best_model.joblib ADDED Viewed

	@@ -0,0 +1,3 @@

+version https://git-lfs.github.com/spec/v1
+oid sha256:370a1b9437b5c3cd19fb983d819a9b554a1bfd8b1a5a9dd130cc110cfb05f067
+size 321499

requirements.txt ADDED Viewed

	@@ -0,0 +1,437 @@

+absl-py==1.4.0
+aiofiles==23.1.0
+aiohttp==3.8.4
+aiosignal==1.3.1
+alabaster==0.7.13
+albumentations==1.2.1
+altair==4.2.2
+anyio==3.7.0
+appdirs==1.4.4
+argon2-cffi==21.3.0
+argon2-cffi-bindings==21.2.0
+array-record==0.4.0
+arviz==0.15.1
+astropy==5.2.2
+astunparse==1.6.3
+async-timeout==4.0.2
+attrs==23.1.0
+audioread==3.0.0
+autograd==1.6.1
+Babel==2.12.1
+backcall==0.2.0
+beautifulsoup4==4.11.2
+bleach==6.0.0
+blis==0.7.9
+blosc2==2.0.0
+bokeh==2.4.3
+branca==0.6.0
+build==0.10.0
+CacheControl==0.13.1
+cached-property==1.5.2
+cachetools==5.3.1
+catalogue==2.0.8
+certifi==2023.5.7
+cffi==1.15.1
+chardet==4.0.0
+charset-normalizer==2.0.12
+chex==0.1.7
+click==8.1.3
+click-plugins==1.1.1
+cligj==0.7.2
+cloudpickle==2.2.1
+cmake==3.25.2
+cmdstanpy==1.1.0
+colorcet==3.0.1
+colorlover==0.3.0
+community==1.0.0b1
+confection==0.0.4
+cons==0.4.6
+contextlib2==0.6.0.post1
+contourpy==1.1.0
+convertdate==2.4.0
+cufflinks==0.17.3
+cvxopt==1.3.1
+cvxpy==1.3.1
+cycler==0.11.0
+cymem==2.0.7
+Cython==0.29.35
+dask==2022.12.1
+datascience==0.17.6
+db-dtypes==1.1.1
+dbus-python==1.2.16
+debugpy==1.6.6
+decorator==4.4.2
+defusedxml==0.7.1
+distributed==2022.12.1
+dlib==19.24.2
+dm-tree==0.1.8
+docutils==0.16
+dopamine-rl==4.0.6
+duckdb==0.8.1
+earthengine-api==0.1.357
+easydict==1.10
+ecos==2.0.12
+editdistance==0.6.2
+en-core-web-sm @ https://github.com/explosion/spacy-models/releases/download/en_core_web_sm-3.5.0/en_core_web_sm-3.5.0-py3-none-any.whl#sha256=0964370218b7e1672a30ac50d72cdc6b16f7c867496f1d60925691188f4d2510
+entrypoints==0.4
+ephem==4.1.4
+et-xmlfile==1.1.0
+etils==1.3.0
+etuples==0.3.9
+exceptiongroup==1.1.1
+fastai==2.7.12
+fastapi==0.100.0
+fastcore==1.5.29
+fastdownload==0.0.7
+fastjsonschema==2.17.1
+fastprogress==1.0.3
+fastrlock==0.8.1
+ffmpy==0.3.0
+filelock==3.12.2
+Fiona==1.9.4.post1
+firebase-admin==5.3.0
+Flask==2.2.5
+flatbuffers==23.5.26
+flax==0.6.11
+folium==0.14.0
+fonttools==4.40.0
+frozendict==2.3.8
+frozenlist==1.3.3
+fsspec==2023.6.0
+future==0.18.3
+gast==0.4.0
+gcsfs==2023.6.0
+GDAL==3.3.2
+gdown==4.6.6
+gensim==4.3.1
+geographiclib==2.0
+geopandas==0.13.2
+geopy==2.3.0
+gin-config==0.5.0
+glob2==0.7
+google==2.0.3
+google-api-core==2.11.1
+google-api-python-client==2.84.0
+google-auth==2.17.3
+google-auth-httplib2==0.1.0
+google-auth-oauthlib==1.0.0
+google-cloud-bigquery==3.10.0
+google-cloud-bigquery-connection==1.12.0
+google-cloud-bigquery-storage==2.20.0
+google-cloud-core==2.3.2
+google-cloud-datastore==2.15.2
+google-cloud-firestore==2.11.1
+google-cloud-functions==1.13.0
+google-cloud-language==2.9.1
+google-cloud-storage==2.8.0
+google-cloud-translate==3.11.1
+google-colab @ file:///colabtools/dist/google-colab-1.0.0.tar.gz#sha256=65ade0f57298e1ede1ee6fe8aaf53f99c32267220c3fd7615c9c7848d2597b9b
+google-crc32c==1.5.0
+google-pasta==0.2.0
+google-resumable-media==2.5.0
+googleapis-common-protos==1.59.1
+googledrivedownloader==0.4
+gradio==3.36.1
+gradio_client==0.2.7
+graphviz==0.20.1
+greenlet==2.0.2
+grpc-google-iam-v1==0.12.6
+grpcio==1.56.0
+grpcio-status==1.48.2
+gspread==3.4.2
+gspread-dataframe==3.0.8
+gym==0.25.2
+gym-notices==0.0.8
+h11==0.14.0
+h5netcdf==1.2.0
+h5py==3.8.0
+holidays==0.27.1
+holoviews==1.15.4
+html5lib==1.1
+httpcore==0.17.3
+httpimport==1.3.0
+httplib2==0.21.0
+httpx==0.24.1
+huggingface-hub==0.16.4
+humanize==4.6.0
+hyperopt==0.2.7
+idna==3.4
+imageio==2.25.1
+imageio-ffmpeg==0.4.8
+imagesize==1.4.1
+imbalanced-learn==0.10.1
+imgaug==0.4.0
+importlib-resources==5.12.0
+imutils==0.5.4
+inflect==6.0.4
+iniconfig==2.0.0
+intel-openmp==2023.1.0
+ipykernel==5.5.6
+ipython==7.34.0
+ipython-genutils==0.2.0
+ipython-sql==0.4.1
+ipywidgets==7.7.1
+itsdangerous==2.1.2
+jax==0.4.10
+jaxlib @ https://storage.googleapis.com/jax-releases/cuda11/jaxlib-0.4.10+cuda11.cudnn86-cp310-cp310-manylinux2014_x86_64.whl#sha256=fe53205ef12727c80ed5ac2d4506d6732c0c3db69ede4565a7d4df98e609af84
+jieba==0.42.1
+Jinja2==3.1.2
+joblib==1.2.0
+jsonpickle==3.0.1
+jsonschema==4.3.3
+jupyter-client==6.1.12
+jupyter-console==6.1.0
+jupyter-server==1.24.0
+jupyter_core==5.3.1
+jupyterlab-pygments==0.2.2
+jupyterlab-widgets==3.0.7
+kaggle==1.5.13
+keras==2.12.0
+kiwisolver==1.4.4
+langcodes==3.3.0
+lazy_loader==0.2
+libclang==16.0.0
+librosa==0.10.0.post2
+lightgbm==3.3.5
+linkify-it-py==2.0.2
+lit==16.0.6
+llvmlite==0.39.1
+locket==1.0.0
+logical-unification==0.4.6
+LunarCalendar==0.0.9
+lxml==4.9.2
+Markdown==3.4.3
+markdown-it-py==2.2.0
+MarkupSafe==2.1.3
+matplotlib==3.7.1
+matplotlib-inline==0.1.6
+matplotlib-venn==0.11.9
+mdit-py-plugins==0.3.3
+mdurl==0.1.2
+miniKanren==1.0.3
+missingno==0.5.2
+mistune==0.8.4
+mizani==0.8.1
+mkl==2019.0
+ml-dtypes==0.2.0
+mlxtend==0.14.0
+more-itertools==9.1.0
+moviepy==1.0.3
+mpmath==1.3.0
+msgpack==1.0.5
+multidict==6.0.4
+multipledispatch==0.6.0
+multitasking==0.0.11
+murmurhash==1.0.9
+music21==8.1.0
+natsort==8.3.1
+nbclient==0.8.0
+nbconvert==6.5.4
+nbformat==5.9.0
+nest-asyncio==1.5.6
+networkx==3.1
+nibabel==3.0.2
+nltk==3.8.1
+notebook==6.4.8
+numba==0.56.4
+numexpr==2.8.4
+numpy==1.22.4
+oauth2client==4.1.3
+oauthlib==3.2.2
+opencv-contrib-python==4.7.0.72
+opencv-python==4.7.0.72
+opencv-python-headless==4.7.0.72
+openpyxl==3.0.10
+opt-einsum==3.3.0
+optax==0.1.5
+orbax-checkpoint==0.2.6
+orjson==3.9.2
+osqp==0.6.2.post8
+packaging==23.1
+palettable==3.3.3
+pandas==1.5.3
+pandas-datareader==0.10.0
+pandas-gbq==0.17.9
+pandocfilters==1.5.0
+panel==0.14.4
+param==1.13.0
+parso==0.8.3
+partd==1.4.0
+pathlib==1.0.1
+pathy==0.10.2
+patsy==0.5.3
+pexpect==4.8.0
+pickleshare==0.7.5
+Pillow==8.4.0
+pip-tools==6.13.0
+platformdirs==3.7.0
+plotly==5.13.1
+plotnine==0.10.1
+pluggy==1.2.0
+polars==0.17.3
+pooch==1.6.0
+portpicker==1.5.2
+prefetch-generator==1.0.3
+preshed==3.0.8
+prettytable==0.7.2
+proglog==0.1.10
+progressbar2==4.2.0
+prometheus-client==0.17.0
+promise==2.3
+prompt-toolkit==3.0.38
+prophet==1.1.4
+proto-plus==1.22.3
+protobuf==3.20.3
+psutil==5.9.5
+psycopg2==2.9.6
+ptyprocess==0.7.0
+py-cpuinfo==9.0.0
+py4j==0.10.9.7
+pyarrow==9.0.0
+pyasn1==0.5.0
+pyasn1-modules==0.3.0
+pycocotools==2.0.6
+pycparser==2.21
+pyct==0.5.0
+pydantic==1.10.9
+pydata-google-auth==1.8.0
+pydot==1.4.2
+pydot-ng==2.0.0
+pydotplus==2.0.2
+PyDrive==1.3.1
+pydub==0.25.1
+pyerfa==2.0.0.3
+pygame==2.4.0
+Pygments==2.14.0
+PyGObject==3.36.0
+pymc==5.1.2
+PyMeeus==0.5.12
+pymystem3==0.2.0
+PyOpenGL==3.1.7
+pyparsing==3.1.0
+pyproj==3.6.0
+pyproject_hooks==1.0.0
+pyrsistent==0.19.3
+PySocks==1.7.1
+pytensor==2.10.1
+pytest==7.2.2
+python-apt==0.0.0
+python-dateutil==2.8.2
+python-louvain==0.16
+python-multipart==0.0.6
+python-slugify==8.0.1
+python-utils==3.7.0
+pytz==2022.7.1
+pyviz-comms==2.3.2
+PyWavelets==1.4.1
+PyYAML==6.0
+pyzmq==23.2.1
+qdldl==0.1.7
+qudida==0.0.4
+regex==2022.10.31
+requests==2.27.1
+requests-oauthlib==1.3.1
+requests-unixsocket==0.2.0
+requirements-parser==0.5.0
+rich==13.4.2
+rpy2==3.5.5
+rsa==4.9
+scikit-image==0.19.3
+scikit-learn==1.2.2
+scipy==1.10.1
+scs==3.2.3
+seaborn==0.12.2
+semantic-version==2.10.0
+Send2Trash==1.8.2
+shapely==2.0.1
+six==1.16.0
+sklearn-pandas==2.2.0
+smart-open==6.3.0
+sniffio==1.3.0
+snowballstemmer==2.2.0
+sortedcontainers==2.4.0
+soundfile==0.12.1
+soupsieve==2.4.1
+soxr==0.3.5
+spacy==3.5.3
+spacy-legacy==3.0.12
+spacy-loggers==1.0.4
+Sphinx==3.5.4
+sphinxcontrib-applehelp==1.0.4
+sphinxcontrib-devhelp==1.0.2
+sphinxcontrib-htmlhelp==2.0.1
+sphinxcontrib-jsmath==1.0.1
+sphinxcontrib-qthelp==1.0.3
+sphinxcontrib-serializinghtml==1.1.5
+SQLAlchemy==2.0.16
+sqlparse==0.4.4
+srsly==2.4.6
+starlette==0.27.0
+statsmodels==0.13.5
+sympy==1.11.1
+tables==3.8.0
+tabulate==0.8.10
+tblib==2.0.0
+tenacity==8.2.2
+tensorboard==2.12.3
+tensorboard-data-server==0.7.1
+tensorflow==2.12.0
+tensorflow-datasets==4.9.2
+tensorflow-estimator==2.12.0
+tensorflow-gcs-config==2.12.0
+tensorflow-hub==0.13.0
+tensorflow-io-gcs-filesystem==0.32.0
+tensorflow-metadata==1.13.1
+tensorflow-probability==0.20.1
+tensorstore==0.1.38
+termcolor==2.3.0
+terminado==0.17.1
+text-unidecode==1.3
+textblob==0.17.1
+tf-slim==1.1.0
+thinc==8.1.10
+threadpoolctl==3.1.0
+tifffile==2023.4.12
+tinycss2==1.2.1
+toml==0.10.2
+tomli==2.0.1
+toolz==0.12.0
+torch @ https://download.pytorch.org/whl/cu118/torch-2.0.1%2Bcu118-cp310-cp310-linux_x86_64.whl#sha256=a7a49d459bf4862f64f7bc1a68beccf8881c2fa9f3e0569608e16ba6f85ebf7b
+torchaudio @ https://download.pytorch.org/whl/cu118/torchaudio-2.0.2%2Bcu118-cp310-cp310-linux_x86_64.whl#sha256=26692645ea061a005c57ec581a2d0425210ac6ba9f923edf11cc9b0ef3a111e9
+torchdata==0.6.1
+torchsummary==1.5.1
+torchtext==0.15.2
+torchvision @ https://download.pytorch.org/whl/cu118/torchvision-0.15.2%2Bcu118-cp310-cp310-linux_x86_64.whl#sha256=19ca4ab5d6179bbe53cff79df1a855ee6533c2861ddc7389f68349d8b9f8302a
+tornado==6.3.1
+tqdm==4.65.0
+traitlets==5.7.1
+triton==2.0.0
+tweepy==4.13.0
+typer==0.7.0
+types-setuptools==68.0.0.1
+typing_extensions==4.6.3
+tzlocal==5.0.1
+uc-micro-py==1.0.2
+uritemplate==4.1.1
+urllib3==1.26.16
+uvicorn==0.22.0
+vega-datasets==0.9.0
+wasabi==1.1.2
+wcwidth==0.2.6
+webcolors==1.13
+webencodings==0.5.1
+websocket-client==1.6.0
+websockets==11.0.3
+Werkzeug==2.3.6
+widgetsnbextension==3.6.4
+wordcloud==1.8.2.2
+wrapt==1.14.1
+xarray==2022.12.0
+xarray-einstats==0.5.1
+xgboost==1.7.6
+xlrd==2.0.1
+yarl==1.9.2
+yellowbrick==1.5
+yfinance==0.2.21
+zict==3.0.0
+zipp==3.15.0