SkLearn Linear Regression (Housing Prices Example)




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#LinearRegression #HousingPrices #ScikitLearn #DataScience #MachineLearning #DataAnalytics

We will be learning how we use sklearn library in python to apply machine learning algorithms in python.

scikit learn has Linear Regression in linear model class. Regression can be used for predicting any kind of data. In this tutorial we use regression for predicting housing prices in the boston dataset present in the sklearn datasets.

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Comment List

  • The Semicolon
    November 14, 2020

    Finally a simple demo
    Thank you from all beginners!

  • The Semicolon
    November 14, 2020

    How to predict data if the data is in string format like "Brand of the care" on the basis of "Price and Hourse Power"?

  • The Semicolon
    November 14, 2020

    This was very useful. Thank you

  • The Semicolon
    November 14, 2020

    Hello i need help in python.
    I have dataset in which 3 features and i want to predict the 3rd feature on base of first 2 features.

  • The Semicolon
    November 14, 2020

    tnq

  • The Semicolon
    November 14, 2020

    tnq

  • The Semicolon
    November 14, 2020

    where's the sets?

  • The Semicolon
    November 14, 2020

    How do you manipulate the parameters ???

  • The Semicolon
    November 14, 2020

    2019+ Use…
    sklearn.model_selection
    not
    sklearn.cross_validation

  • The Semicolon
    November 14, 2020

    Sir, hoe can i get the dataset of india for house price prediction because i don't get it on kaggle or in any website..

  • The Semicolon
    November 14, 2020

    Where are the p-values for the regression coefficients?

  • The Semicolon
    November 14, 2020

    a=reg.predict(x_test)
    error :ValueError: shapes (102,1) and (13,1) not aligned: 1 (dim 1) != 13 (dim 0)

  • The Semicolon
    November 14, 2020

    Can anyone help me out ???

    import numpy as np
    import pandas as pd
    import matplotlib.pyplot as plt
    import sklearn

    from sklearn.datasets import load_boston
    boston = load_boston()

    x = pd.DataFrame(boston.data,columns=boston.feature_names)
    y = pd.DataFrame(boston.target)

    from sklearn.model_selection import train_test_split
    x_train,y_train,x_test,y_test = train_test_split(x,y,test_size=1/3,random_state=3)

    from sklearn.linear_model import LinearRegression
    reg = LinearRegression()

    reg.fit(x_train,y_train)

  • The Semicolon
    November 14, 2020

    i guess you should speak a bit more loud

  • The Semicolon
    November 14, 2020

    If you have an error use 'from sklearn.model_selection import train_test_split'

  • The Semicolon
    November 14, 2020

    To help, sklearn.cross_validation is depricated, now the uses is sklearn.model_selection

  • The Semicolon
    November 14, 2020

    Straight to the point. Excellent!!!

  • The Semicolon
    November 14, 2020

    9 minute video packed with great value. Bow!

  • The Semicolon
    November 14, 2020

    short & sweet explaination

  • The Semicolon
    November 14, 2020

    hey just came across this..quite basic informative, but a question that arent we suppose to compare " a vs y_train" instead of y_test?

  • The Semicolon
    November 14, 2020

    hey
    awesome videos
    keep making the good stuff!!!

    i tried the code and made a scatter plot
    and the regression line was not accurate as you are saying the error is high !!

  • The Semicolon
    November 14, 2020

    what do you mean by better model ? what other type of better models available in scikitlearn.

  • The Semicolon
    November 14, 2020

    Is it required to normalize the data before training? This is one confusion I always have when building my models.

  • The Semicolon
    November 14, 2020

    Please visualize the test set data.

  • The Semicolon
    November 14, 2020

    Bro you have taught pandas, Numpy, and Matplotlib but did not teach Sckit-learn before this?

  • The Semicolon
    November 14, 2020

    Why coefficient used here

  • The Semicolon
    November 14, 2020

    sir can you help me there is no boston dataset in my anaconda I install all the libraries you wrote in the video

  • The Semicolon
    November 14, 2020

    So the output of reg.predict(x_test) are the next values of price? and what does the left column in dataframe [y_test] mean?

  • The Semicolon
    November 14, 2020

    simply surpb

  • The Semicolon
    November 14, 2020

    Good tutorial… Simple and crisp. Great for a beginner like me .. Thanks 🙂

  • The Semicolon
    November 14, 2020

    sir what is traning data and test data im beg im so confuse

  • The Semicolon
    November 14, 2020

    Very useful pointers that make it easy to learn and remember. Thank you. Please start a new series doing the datasets from Kaggle and UCI for both Classification and Regression if possible. I'd be indebted to you in terms of gratitude.

  • The Semicolon
    November 14, 2020

    it sounds pretty good 🙂

  • The Semicolon
    November 14, 2020

    can we use other dataset instead of boston dataset if yes then how?please tell me

  • The Semicolon
    November 14, 2020

    Hello,I have a similar task and I'm having trouble performing it, can you help me in detail

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