Create a linear regression model within 20 minutes in Python

Question

This question was asked in one of the interviews where the ask was to create a Linear Regression model from importing library to the final prediction within 20 minutes.

The objective was to check if you can think fast and are fluent in basics of model building.

Even better, create your own dataset with 5 independent variable in MS Excel

 

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TheDataMonk 3 years 2 Answers 685 views Grand Master 0

Answers ( 2 )

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    data = pd.read_csv(‘data.csv’) # load data set
    X = data.iloc[:, 0].values.reshape(-1, 1) # values converts it into a numpy array
    Y = data.iloc[:, 1].values.reshape(-1, 1) # -1 means that calculate the dimension of rows, but have 1 column
    linear_regressor = LinearRegression() # create object for the class
    linear_regressor.fit(X, Y) # perform linear regression
    Y_pred = linear_regressor.predict(X) # make predictions

  2. Shivani agarwal
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    # i have created a simple model to predict height on the basis of weight and gender.

    import pandas as pd
    import numpy as np
    from sklearn.linear_model import LinearRegression
    from sklearn.model_selection import train_test_split

    data = pd.read_csv(r”data.csv”)
    print(data)

    #check for missing values
    data.isnull().sum()

    # encoding hot encoder
    data[“Sex”] = np.where(data[“Gender”] ==”F” , 1, 0)
    data.drop([“Gender”] , axis = 1)

    correlation = data.corr()

    X = data.drop([“Height”, “Gender”], axis = 1)
    Y = data[“Height”]

    X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=0.2, random_state=42)
    print(X_train)

    lm = LinearRegression().fit(X_train, y_train)
    y_predict = lm.predict(X_test)
    print(y_predict)
    print(“regression coefficient”, lm.score(X_train, y_train))

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