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import pandas as pd |
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import numpy as np |
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import seaborn as sn |
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import matplotlib.pyplot as plt |
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import gradio as gr |
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df=pd.read_csv('Placement_Data_Full_Class.csv') |
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df.drop(columns=['sl_no'],axis=1,inplace=True) |
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df_encoded=pd.get_dummies(df,columns=['gender','ssc_b',"hsc_b","workex","hsc_s","degree_t","specialisation","status"],prefix=['gender','ssc_b',"hsc_b","workex","hsc_s","degree_t","specialisation","status"], drop_first=True) |
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df_encoded.drop(columns='salary',axis=1,inplace=True) |
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test=df_encoded['status_Placed'] |
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train=df_encoded.drop(columns='status_Placed',axis=1) |
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from sklearn.linear_model import LogisticRegression |
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from sklearn.tree import DecisionTreeClassifier |
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from sklearn.ensemble import RandomForestClassifier |
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from sklearn.svm import SVC |
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from sklearn import tree |
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from sklearn.model_selection import train_test_split |
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from sklearn import metrics |
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x_train,x_test,y_train,y_test=train_test_split(train,test,test_size=0.2,random_state=0) |
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from sklearn.preprocessing import StandardScaler |
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ss=StandardScaler() |
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x_train=ss.fit_transform(x_train) |
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x_test=ss.transform(x_test) |
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lr = LogisticRegression() |
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lr.fit(x_train,y_train) |
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def predict_placement_status(ssc_p,hsc_p,degree_p,etest_p,mba_p,gender_M,ssc_b_Others,hsc_b_Others,workex_Yes,hsc_s_Commerce,hsc_s_Science,degree_t_Others,degree_t_Sci_Tech,specialisation_Mkt_HR): |
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gender_M = 1 if gender_M == "Male" else 0 |
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ssc_b_Others = 1 if ssc_b_Others == "Others" else 0 |
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hsc_b_Others = 1 if hsc_b_Others == "Others" else 0 |
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workex_Yes = 1 if workex_Yes == "Yes" else 0 |
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hsc_s_Commerce = 1 if hsc_s_Commerce == "Commerce" else 0 |
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hsc_s_Science = 1 if hsc_s_Science == "Science" else 0 |
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degree_t_Others = 1 if degree_t_Others == "Others" else 0 |
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degree_t_Sci_Tech = 1 if degree_t_Sci_Tech == "Sci&Tech" else 0 |
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specialisation_Mkt_HR = 1 if specialisation_Mkt_HR == "Mkt&HR" else 0 |
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input_data = [[float(ssc_p),float(hsc_p),float(degree_p),float(etest_p),float(mba_p),gender_M,ssc_b_Others,hsc_b_Others,workex_Yes,hsc_s_Commerce,hsc_s_Science,degree_t_Others,degree_t_Sci_Tech,specialisation_Mkt_HR]] |
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input_data_scaled = ss.transform(input_data) |
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prediction = lr.predict(input_data_scaled) |
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return "Placed" if prediction[0] == 1 else "Not Placed" |
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iface = gr.Interface(fn=predict_placement_status, |
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inputs=[gr.Number(label="SSC Marks"), |
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gr.Number(label="HSC Marks"), |
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gr.Number(label="Degree Marks"), |
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gr.Number(label="Emploibility-test Marks"), |
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gr.Number(label="MBA Marks"), |
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gr.Radio(label="Gender", choices=["Female", "Male"]), |
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gr.Radio(label="SSC Board", choices=["Central", "Others"]), |
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gr.Radio(label="HSC Board", choices=["Central", "Others"]), |
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gr.Radio(label="Work Experience", choices=["No", "Yes"]), |
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gr.Radio(label="HSC Stream", choices=["Commerce", "Science", "Arts"]), |
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gr.Radio(label="HSC Stream", choices=["Commerce", "Science", "Arts"]), |
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gr.Radio(label="Degree Type", choices=["Others", "Sci&Tech", "Comm&Mgmt"]), |
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gr.Radio(label="Degree Type", choices=["Others", "Sci&Tech", "Comm&Mgmt"]), |
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gr.Radio(label="Specialisation", choices=["Mkt&HR", "Mkt&Fin"]), |
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], |
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outputs="text", |
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title="Prediction", |
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description="Predict based on selected features.",theme='compact' |
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) |
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iface.launch(share=True) |
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