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| import pandas as pd | |
| import numpy as np | |
| import matplotlib.pyplot as plt | |
| from sklearn.model_selection import train_test_split | |
| from sklearn.linear_model import LinearRegression | |
| import gradio as gr | |
| df=pd.read_excel("Salary.xlsx") | |
| df.head(3) | |
| y_dep=df.Salary | |
| x_ind=df.drop(["Salary","Student"],axis=1) | |
| x_train, x_test, y_train, y_test = train_test_split(x_ind,y_dep, test_size = 0.2,random_state=1) | |
| model = LinearRegression() | |
| model.fit(x_train, y_train) | |
| y_pred = model.predict(x_test) | |
| def salary(school_ranking,gpa,experience): | |
| input1=np.array([school_ranking,gpa,experience]) | |
| output1=model.predict([input1]) | |
| return output1[0].round() | |
| interface = gr.Interface(fn = salary, | |
| inputs=[gr.inputs.Number(default=1, label="School Ranking"), gr.inputs.Slider(1,10,step=0.1,label = "G.P.A"),gr.inputs.Slider(1,15,step=1,label = "Experience")], | |
| outputs = [gr.outputs.Textbox( label="Expected Salary")],description="SALARY PREDICTION") | |
| interface.launch() |