| import gradio as gr |
| import pandas as pd |
| import numpy as np |
|
|
| from sklearn.model_selection import train_test_split |
| from sklearn.preprocessing import StandardScaler, OneHotEncoder |
| from sklearn.compose import ColumnTransformer |
| from sklearn.pipeline import Pipeline |
| from sklearn.multioutput import MultiOutputClassifier |
| from sklearn.metrics import accuracy_score |
| from sklearn.tree import DecisionTreeClassifier |
| from sklearn.ensemble import RandomForestClassifier |
| from sklearn.neighbors import KNeighborsClassifier |
| from sklearn.linear_model import LogisticRegression |
| from sklearn.svm import SVC |
|
|
| |
| df = pd.read_csv("Balanced_Placement_Data.csv") |
|
|
| features = [ |
| 'ssc_percentage', 'hsc_percentage', 'undergrad_degree', 'Graduate_degree_percentage', |
| 'emp_test_percentage', 'Internship_Experience_Months', 'Certifications_Count', |
| 'Technical_Skills_Score', 'Soft_Skills_Score', 'Hackathons_Participated', |
| 'Resume_Score', 'Online_Course_Count', 'Social_Media_Presence' |
| ] |
| target_columns = ['Placement_Status', 'Domain_of_Interest'] |
|
|
| X = df[features] |
| y = df[target_columns] |
|
|
| categorical_features = ['undergrad_degree'] |
| numerical_features = list(set(features) - set(categorical_features)) |
|
|
| preprocessor = ColumnTransformer([ |
| ("num", StandardScaler(), numerical_features), |
| ("cat", OneHotEncoder(drop="first"), categorical_features) |
| ]) |
|
|
| models = { |
| "Random Forest": RandomForestClassifier(random_state=42), |
| "Decision Tree": DecisionTreeClassifier(random_state=42), |
| "KNN": KNeighborsClassifier(), |
| "Logistic Regression": LogisticRegression(max_iter=1000), |
| "SVM": SVC(probability=True) |
| } |
|
|
| |
| def train_models(): |
| X_train, X_test, y_train, y_test = train_test_split( |
| X, y, test_size=0.2, random_state=42 |
| ) |
| results = {} |
| for name, clf in models.items(): |
| pipe = Pipeline([ |
| ("preprocessor", preprocessor), |
| ("classifier", MultiOutputClassifier(clf)) |
| ]) |
| pipe.fit(X_train, y_train) |
| y_pred = pipe.predict(X_test) |
|
|
| placement_acc = accuracy_score(y_test["Placement_Status"], y_pred[:,0]) |
| domain_acc = accuracy_score(y_test["Domain_of_Interest"], y_pred[:,1]) |
|
|
| results[name] = { |
| "Placement Accuracy": placement_acc, |
| "Domain Accuracy": domain_acc, |
| "Model": pipe |
| } |
| return results |
|
|
| results = train_models() |
| best_model_name = max(results, key=lambda m: results[m]["Placement Accuracy"] + results[m]["Domain Accuracy"]) |
| best_model = results[best_model_name]["Model"] |
|
|
| |
| def predict_placement_and_domain( |
| ssc_percentage, hsc_percentage, undergrad_degree, Graduate_degree_percentage, |
| emp_test_percentage, Internship_Experience_Months, Certifications_Count, |
| Technical_Skills_Score, Soft_Skills_Score, Hackathons_Participated, |
| Resume_Score, Online_Course_Count, Social_Media_Presence |
| ): |
| user_input = { |
| "ssc_percentage": ssc_percentage, |
| "hsc_percentage": hsc_percentage, |
| "undergrad_degree": undergrad_degree, |
| "Graduate_degree_percentage": Graduate_degree_percentage, |
| "emp_test_percentage": emp_test_percentage, |
| "Internship_Experience_Months": Internship_Experience_Months, |
| "Certifications_Count": Certifications_Count, |
| "Technical_Skills_Score": Technical_Skills_Score, |
| "Soft_Skills_Score": Soft_Skills_Score, |
| "Hackathons_Participated": Hackathons_Participated, |
| "Resume_Score": Resume_Score, |
| "Online_Course_Count": Online_Course_Count, |
| "Social_Media_Presence": Social_Media_Presence |
| } |
|
|
| input_df = pd.DataFrame([user_input]) |
| prediction = best_model.predict(input_df) |
|
|
| return { |
| "Placement Status": prediction[0][0], |
| "Domain of Interest": prediction[0][1], |
| "Best Model": best_model_name |
| } |
|
|
| |
| with gr.Blocks() as demo: |
| gr.Markdown("# 🎯 Placement & Domain Predictor") |
|
|
| with gr.Row(): |
| with gr.Column(): |
| ssc_percentage = gr.Number(label="SSC Percentage", value=70) |
| hsc_percentage = gr.Number(label="HSC Percentage", value=65) |
| undergrad_degree = gr.Dropdown(choices=list(df['undergrad_degree'].unique()), label="Undergrad Degree") |
| Graduate_degree_percentage = gr.Number(label="Graduate Degree %", value=60) |
| emp_test_percentage = gr.Number(label="Employment Test %", value=50) |
| Internship_Experience_Months = gr.Number(label="Internship Months", value=0) |
| Certifications_Count = gr.Number(label="Certifications Count", value=1) |
| Technical_Skills_Score = gr.Number(label="Technical Skills Score", value=60) |
| Soft_Skills_Score = gr.Number(label="Soft Skills Score", value=60) |
| Hackathons_Participated = gr.Number(label="Hackathons Participated", value=1) |
| Resume_Score = gr.Number(label="Resume Score", value=50) |
| Online_Course_Count = gr.Number(label="Online Course Count", value=2) |
| Social_Media_Presence = gr.Number(label="Social Media Presence (0/1)", value=1) |
|
|
| btn = gr.Button("Predict") |
|
|
| with gr.Column(): |
| output = gr.JSON(label="Prediction Result") |
|
|
| btn.click( |
| predict_placement_and_domain, |
| inputs=[ssc_percentage, hsc_percentage, undergrad_degree, Graduate_degree_percentage, |
| emp_test_percentage, Internship_Experience_Months, Certifications_Count, |
| Technical_Skills_Score, Soft_Skills_Score, Hackathons_Participated, |
| Resume_Score, Online_Course_Count, Social_Media_Presence], |
| outputs=output |
| ) |
|
|
| demo.launch() |
|
|
