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import pandas as pd |
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import streamlit as st |
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import base64 |
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def main(): |
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try: |
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html_temp = """ |
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<div style="background-color:tomato;padding:10px"> |
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<h2 style="color:white;text-align:center;">Automatic Machine Learning </h2> |
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</div> |
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""" |
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st.markdown(html_temp,unsafe_allow_html=True) |
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from PIL import Image |
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image_loan=Image.open("ml4.jpg") |
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st.sidebar.title("Upload Input csv file : ") |
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file_upload=st.sidebar.file_uploader(" ",type=["csv"]) |
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st.sidebar.image(image_loan,use_column_width=True) |
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if file_upload is not None: |
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f1=pd.read_csv(file_upload) |
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f1.isna().sum() |
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f1=f1.dropna() |
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X=f1[['age','previous_year_rating','length_of_service','KPI_Met','awards_won']] |
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y=f1['is_promoted'] |
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d2=f1[['age','previous_year_rating','length_of_service','KPI_Met','awards_won','is_promoted']] |
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st.text(" ") |
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if st.checkbox('Show Input Data'): |
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st.write(d2) |
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st.subheader("Pick Your Algorithm") |
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choose_model=st.selectbox(label=' ',options=[' ','Random Forest','Logistic Regression']) |
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if (choose_model=='Random Forest'): |
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from sklearn.model_selection import train_test_split |
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X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.3,random_state=0) |
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from sklearn.ensemble import RandomForestClassifier |
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classifier=RandomForestClassifier() |
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classifier.fit(X_train, y_train) |
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y_pred=classifier.predict(X_test) |
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from sklearn.metrics import accuracy_score |
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score=accuracy_score(y_test,y_pred) |
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from sklearn.metrics import confusion_matrix |
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c1=confusion_matrix(y_test,y_pred) |
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st.write("Model Accuracy : ", score) |
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st.write("Confusion Matrix : ", c1) |
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from sklearn.model_selection import cross_val_score |
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cv=cross_val_score(classifier,X_train, y_train,cv=5,scoring='accuracy') |
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st.write("Cross Calidation of Model : ", cv) |
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import matplotlib.pyplot as plt |
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from sklearn import metrics |
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y_pred_proba = classifier.predict_proba(X_test)[::,1] |
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fpr, tpr, _ = metrics.roc_curve(y_test, y_pred_proba) |
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auc = metrics.roc_auc_score(y_test, y_pred_proba) |
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plt.plot(fpr,tpr,label="data 1, auc="+str(auc)) |
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plt.legend(loc=4) |
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st.subheader("ROC Curve") |
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st.pyplot(plt) |
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st.subheader("Upload csv file for Predictions : ") |
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file_upload=st.file_uploader(" ",type=["csv"]) |
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if file_upload is not None: |
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data=pd.read_csv(file_upload) |
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data1=data.dropna() |
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data=data1[['age','previous_year_rating','length_of_service','KPI_Met','awards_won']] |
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predictions=classifier.predict(data) |
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data['employee_id'] = data1['employee_id'] |
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data['Prediction'] = predictions |
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st.subheader("Find the Predicted Results below :") |
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st.write(data) |
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st.text("0 : Not Eligible for Promotion") |
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st.text("1 : Eligible for Promotion") |
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csv = data.to_csv(index=False) |
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b64 = base64.b64encode(csv.encode()).decode() |
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href = f'<a href="data:file/csv;base64,{b64}">Download The Prediction Results CSV File</a> (right-click and save as <some_name>.csv)' |
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st.markdown(href, unsafe_allow_html=True) |
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display_df = st.checkbox(label='Visualize the Predicted Value') |
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if display_df: |
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st.bar_chart(data['Prediction'].value_counts()) |
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st.text(data['Prediction'].value_counts()) |
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if (choose_model=='Logistic Regression'): |
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from sklearn.model_selection import train_test_split |
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X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.3,random_state=0) |
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from sklearn.linear_model import LogisticRegression |
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classifier = LogisticRegression() |
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classifier.fit(X_train, y_train) |
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y_pred=classifier.predict(X_test) |
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from sklearn.metrics import accuracy_score |
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score=accuracy_score(y_test,y_pred) |
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from sklearn.metrics import confusion_matrix |
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c1=confusion_matrix(y_test,y_pred) |
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st.write("Model Accuracy : ", score) |
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st.write("Confusion Matrix : ", c1) |
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from sklearn.model_selection import cross_val_score |
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cv=cross_val_score(classifier,X_train, y_train,cv=5,scoring='accuracy') |
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st.write("Cross Calidation of Model : ", cv) |
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import matplotlib.pyplot as plt |
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from sklearn import metrics |
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y_pred_proba = classifier.predict_proba(X_test)[::,1] |
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fpr, tpr, _ = metrics.roc_curve(y_test, y_pred_proba) |
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auc = metrics.roc_auc_score(y_test, y_pred_proba) |
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plt.plot(fpr,tpr,label="data 1, auc="+str(auc)) |
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plt.legend(loc=4) |
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st.subheader("ROC Curve") |
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st.pyplot(plt) |
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st.subheader("Upload csv file for Predictions : ") |
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file_upload=st.file_uploader(" ",type=["csv"]) |
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if file_upload is not None: |
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data=pd.read_csv(file_upload) |
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data1=data.dropna() |
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data=data1[['age','previous_year_rating','length_of_service','KPI_Met','awards_won']] |
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predictions=classifier.predict(data) |
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data['employee_id'] = data1['employee_id'] |
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data['Prediction'] = predictions |
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st.subheader("Find the Predicted Results below :") |
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st.write(data) |
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st.text("0 : Not Eligible for Promotion") |
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st.text("1 : Eligible for Promotion") |
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csv = data.to_csv(index=False) |
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b64 = base64.b64encode(csv.encode()).decode() |
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href = f'<a href="data:file/csv;base64,{b64}">Download The Prediction Results CSV File</a> (right-click and save as <some_name>.csv)' |
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st.markdown(href, unsafe_allow_html=True) |
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display_df = st.checkbox(label='Visualize the Predicted Value') |
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if display_df: |
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st.bar_chart(data['Prediction'].value_counts()) |
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st.text(data['Prediction'].value_counts()) |
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except: |
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st.header("An Error occurred") |
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if __name__=='__main__': |
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main() |
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