Spaces:
Build error
Build error
| # -*- coding: utf-8 -*- | |
| """Capstone Gradio App Embedding.ipynb | |
| Automatically generated by Colaboratory. | |
| Original file is located at | |
| https://colab.research.google.com/drive/1zsT_lHGVHzG29XSb4tMF3UdA6glyWnRx | |
| """ | |
| # from google.colab import drive | |
| # drive.mount('/content/drive') | |
| #!pip install gradio | |
| #!pip install category_encoders | |
| """### **DATA PREP**""" | |
| import pandas as pd | |
| import numpy as np | |
| import gradio as gr | |
| from sklearn.model_selection import train_test_split | |
| from sklearn.model_selection import train_test_split, cross_val_score | |
| from sklearn.metrics import accuracy_score, confusion_matrix, recall_score, precision_recall_curve, f1_score | |
| from sklearn.preprocessing import StandardScaler | |
| from sklearn.ensemble import ExtraTreesRegressor | |
| from sklearn.preprocessing import LabelEncoder | |
| import gdown | |
| #train = pd.read_csv('https://drive.google.com/file/d/1U1ic5GE42_cxs8VvuOx7EDaHqtA0oEFC/view?usp=sharing.csv') | |
| #https://drive.google.com/file/d/1U1ic5GE42_cxs8VvuOx7EDaHqtA0oEFC/view?usp=sharing | |
| file_id = '1U1ic5GE42_cxs8VvuOx7EDaHqtA0oEFC' | |
| # Define the URL to download the file | |
| url = f'https://drive.google.com/uc?id={file_id}' | |
| # Download the file and name it 'train.csv' | |
| output_file = 'train.csv' | |
| gdown.download(url, output_file, quiet=False) | |
| # Read the CSV file with Pandas | |
| train = pd.read_csv(output_file) | |
| # use lambda function to remove \t make our model more robst | |
| train = train.applymap(lambda x: x.replace("\t" , '' ) if isinstance (x , str) else x) | |
| # " " , " " | |
| train = train.applymap(lambda x: x.replace(" " , ' ' ) if isinstance (x , str) else x) | |
| # drop what we don't need | |
| train.drop(columns=['MRG', 'user_id', 'ZONE1', 'ZONE2', 'TOP_PACK'], inplace=True) | |
| train["REGION"].fillna(method='ffill', inplace=True) | |
| train["TENURE"].fillna(method='ffill', inplace=True) | |
| train["MONTANT"].fillna(train["MONTANT"].median(), inplace=True) | |
| train["FREQUENCE_RECH"].fillna(0, inplace=True) | |
| train["REVENUE"].fillna(train["REVENUE"].median(), inplace=True) | |
| train["ARPU_SEGMENT"].fillna(0, inplace=True) | |
| train["FREQUENCE"].fillna(0, inplace=True) | |
| train["DATA_VOLUME"].fillna(0, inplace=True) | |
| train["ON_NET"].fillna(0, inplace=True) | |
| train["ORANGE"].fillna(0, inplace=True) | |
| train["TIGO"].fillna(0, inplace=True) | |
| train["REGULARITY"].fillna(train["REGULARITY"].mean(), inplace=True) | |
| train["FREQ_TOP_PACK"].fillna(train["FREQ_TOP_PACK"].mean(), inplace=True) | |
| train['TENURE'] = train['TENURE'].str.replace('D 3-6 month', '1', regex=True) | |
| train['TENURE'] = train['TENURE'].str.replace('E 6-9 month', '2', regex=True) | |
| train['TENURE'] = train['TENURE'].str.replace('F 9-12 month', '3', regex=True) | |
| train['TENURE'] = train['TENURE'].str.replace('J 21-24 month', '4', regex=True) | |
| train['TENURE'] = train['TENURE'].str.replace('G 12-15 month', '5', regex=True) | |
| train['TENURE'] = train['TENURE'].str.replace('H 15-18 month', '6', regex=True) | |
| train['TENURE'] = train['TENURE'].str.replace('I 18-21 month', '7', regex=True) | |
| train['TENURE'] = train['TENURE'].str.replace('K > 24 month', '8', regex=True) | |
| # train['TENURE'].value_counts() | |
| # Define a dictionary to map values | |
| region_mapping = { | |
| 'DAKAR': '1', | |
| 'THIES': '2', | |
| 'SAINT-LOUIS': '3', | |
| 'LOUGA': '4', | |
| 'KAOLACK': '5', | |
| 'DIOURBEL': '6', | |
| 'TAMBACOUNDA': '7', | |
| 'KAFFRINE': '8', | |
| 'KOLDA': '9', | |
| 'FATICK': '10', | |
| 'ZIGUINCHOR': '11', | |
| 'SEDHIOU': '12', | |
| 'KEDOUGOU': '13', | |
| 'MATAM' : '14' | |
| } | |
| # Use the replace method to map values | |
| train['REGION'] = train['REGION'].replace(region_mapping) | |
| # Look at the new value_counts | |
| # print(train['REGION'].value_counts()) | |
| """## **FITTING AND TRAINING**""" | |
| """Select target and features""" | |
| y = train['CHURN'] | |
| x = train.drop(columns='CHURN', axis=1) | |
| X_train, X_test, y_train, y_test = train_test_split(x,y,test_size = 0.5,random_state=45 )# , stratify=y) | |
| #Further split X_train and y_train into train and validation sets | |
| X_train,X_val,y_train,y_val = train_test_split(X_train,y_train,test_size = 0.3, random_state=1 )#, stratify=y) | |
| """### SCALE NUMERICAL COLUMNS""" | |
| num_cols = ['MONTANT', 'FREQUENCE_RECH', 'REVENUE', 'ARPU_SEGMENT', 'FREQUENCE', | |
| 'DATA_VOLUME', 'ON_NET', 'ORANGE', 'TIGO', | |
| 'REGULARITY', 'FREQ_TOP_PACK'] | |
| scaler = StandardScaler() | |
| X_train[num_cols] = scaler.fit_transform(X_train[num_cols]) | |
| X_val[num_cols] = scaler.fit_transform(X_val[num_cols]) | |
| # Create an instance | |
| model = ExtraTreesRegressor( | |
| n_estimators=100, # Number of trees in the forest | |
| max_depth=10, # Maximum depth of the tree | |
| random_state=42 # Random seed for reproducibility | |
| ) | |
| # Train the model | |
| MODEL = model.fit(X_train, y_train) | |
| """## **Check if our model is working**""" | |
| y_pred = MODEL.predict(X_test) | |
| def classifier_1(result): | |
| if result > 0.9: | |
| return "Customer will churn" | |
| else: | |
| return "Customer will not churn" | |
| def predict(REGION,TENURE , MONTANT , FREQUENCE_RECH, REVENUE , ARPU_SEGMENT ,FREQUENCE , DATA_VOLUME , ON_NET, ORANGE , TIGO, REGULARITY ,FREQ_TOP_PACK): | |
| input_array = np.array([[REGION,TENURE , MONTANT , FREQUENCE_RECH, REVENUE , ARPU_SEGMENT ,FREQUENCE , DATA_VOLUME , ON_NET, ORANGE , TIGO, REGULARITY ,FREQ_TOP_PACK]]) | |
| pred = MODEL.predict(input_array) | |
| output = classifier_1 (pred[0]) | |
| if output == "Customer will churn": | |
| return [(0, output)] | |
| else : | |
| return [(1, output)] | |
| #tenure = tenure_dropdown | |
| REGION = gr.inputs.Slider(minimum=1, maximum=13, label='Location of each client') | |
| TENURE = gr.inputs.Slider(minimum=1, maximum=8, label="Duration in network") | |
| MONTANT = gr.inputs.Slider(minimum=22, maximum=470000, label="Top up amount") | |
| FREQUENCE_RECH = gr.inputs.Slider(minimum=1, maximum=131, label="income frequency") | |
| REVENUE = gr.inputs.Slider(minimum=1, maximum=532177, label="ARPU_SEGMENT") | |
| ARPU_SEGMENT = gr.inputs.Slider(minimum=1, maximum= 177392, label="FREQUENCE") | |
| FREQUENCE = gr.inputs.Slider(minimum=1, maximum=91, label="DATA_VOLUME") | |
| DATA_VOLUME =gr.inputs.Slider(minimum=0, maximum=1702309, label="ON_NET") | |
| ON_NET = gr.inputs.Slider(minimum=0, maximum=36687, label="ORANGE") | |
| ORANGE = gr.inputs.Slider(minimum=0, maximum= 6721, label="TIGO") | |
| TIGO = gr.inputs.Slider(minimum=0, maximum=4174, label="ZONE1") | |
| REGULARITY = gr.inputs.Slider(minimum=1, maximum=62, label="ZONE2") | |
| FREQ_TOP_PACK = gr.inputs.Slider(minimum=1, maximum= 592, label="REGULARITY") | |
| op = gr.outputs.HighlightedText(color_map={"Customer will churn":"pink", "Customer will not churn":"yellow"}) | |
| gr.Interface(predict , inputs = [REGION,TENURE, MONTANT , FREQUENCE_RECH, REVENUE , ARPU_SEGMENT ,FREQUENCE , DATA_VOLUME , ON_NET, ORANGE ,TIGO, REGULARITY ,FREQ_TOP_PACK], outputs=op, | |
| live = True).launch(debug=True) | |
| # Input sliders | |
| # REGION = gr.inputs.Slider(minimum=1, maximum=13, label='Location of each client') | |
| # TENURE = gr.inputs.Slider(minimum=1, maximum=8, label="Duration in network") | |
| # MONTANT = gr.inputs.Slider(minimum=22, maximum=470000, label="Top-up amount") | |
| # FREQUENCE_RECH = gr.inputs.Slider(minimum=1, maximum=131, label="Income frequency") | |
| # REVENUE = gr.inputs.Slider(minimum=1, maximum=532177, label="ARPU_SEGMENT") | |
| # ARPU_SEGMENT = gr.inputs.Slider(minimum=1, maximum=177392, label="FREQUENCE") | |
| # FREQUENCE = gr.inputs.Slider(minimum=1, maximum=91, label="DATA_VOLUME") | |
| # DATA_VOLUME = gr.inputs.Slider(minimum=0, maximum=1702309, label="ON_NET") | |
| # ON_NET = gr.inputs.Slider(minimum=0, maximum=36687, label="ORANGE") | |
| # ORANGE = gr.inputs.Slider(minimum=0, maximum=6721, label="TIGO") | |
| # TIGO = gr.inputs.Slider(minimum=0, maximum=4174, label="ZONE1") | |
| # REGULARITY = gr.inputs.Slider(minimum=1, maximum=62, label="ZONE2") | |
| # FREQ_TOP_PACK = gr.inputs.Slider(minimum=1, maximum=592, label="REGULARITY") | |
| # # Output configuration | |
| # op = gr.outputs.HighlightedText(color_map={"Customer will churn": "pink", "Customer will not churn": "yellow"}) | |
| # # Create and launch the interface | |
| # gr.Interface(predict, inputs=[REGION, TENURE, MONTANT, FREQUENCE_RECH, REVENUE, ARPU_SEGMENT, FREQUENCE, | |
| # DATA_VOLUME, ON_NET, ORANGE, TIGO, REGULARITY, FREQ_TOP_PACK], outputs=op, | |
| # live=False).launch(debug=False) | |
| # # Map numerical values to labels | |
| # tenure_labels = { | |
| # 0: "3-6 months", | |
| # 1: "6-9 months", | |
| # 2: "9-12 months", | |
| # 3: "12-15 months", | |
| # 4: "15-18 months", | |
| # 5: "18-21 months", | |
| # 6: "21-24 months", | |
| # 7: "> 24 months" | |
| # } | |
| # # Reverse the mapping for predictions | |
| # tenure_values = {v: k for k, v in tenure_labels.items()} | |
| # # Create a dropdown menu with labels | |
| # tenure_dropdown = gr.inputs.Dropdown(list(tenure_labels.values()), label="TENURE") | |