Multiple Linear Regression in Python - sklearn

Подписаться 3,5 тыс.

Просмотров 78 тыс.

50% 1

Unlock the power of multiple linear regression using Python’s sklearn library with our step-by-step tutorial. This video is designed to help you master the art of predicting outcomes based on multiple variables. Learn how to set up your Python environment, import necessary libraries, and load datasets for analysis. We guide you through the process of fitting a multiple linear regression model, interpreting coefficients, and evaluating model performance with real-world examples. Whether you're a data science enthusiast or a professional looking to enhance your analytical skills, this tutorial provides clear, concise explanations and practical applications. Understand how to handle multicollinearity and improve your model's accuracy with tips and tricks from experts. Subscribe to our channel for more in-depth Python and data science tutorials, and elevate your ability to derive insights from complex datasets with multiple linear regression. Join us and start predicting with precision today!
If you are a complete beginner in machine learning, please watch the video on simple linear regression from this link before and learn the basic concepts first:
• Simple Linear Regressi...
Here is the dataset used in this video:
Please feel free to check out my Data Science blog where you will find a lot of data visualization, exploratory data analysis, statistical analysis, machine learning, natural language processing, and computer vision tutorials and projects:
regenerativetoday.com/
Twitter page:
/ rashida048
Facebook Page:
regenerativetoday.com/
#linearRegression #machinelearning #datascience #dataAnalytics #python #sklearn #jupyternotebook

Опубликовано:

5 май 2022

Ссылка:

Скачать:

Готовим ссылку...

Добавить в:

Мой плейлист

Посмотреть позже

Комментарии : 94

@imveryhungry112 Год назад

im glad people like you exist. I am simply not smart enough to have figured this out on my own

@souravdey1227 Год назад

Very good tutorial. No nonsense and clean. Thanks

@anis.ldx1 5 месяцев назад

Absolutely brilliant! Your way of explaining is beyond exceptional. Thank you so much for this simplistic explanation!

@maheswaraardhani323 4 месяца назад

from the bottom of my heart, i want to thank you for your detailed and easy to follow explanation. i dont know who you are or where you are but you have my utter respect. big thanks

@ShouqAldosari 10 дней назад

thank you very much this helped me a lot hopefully, I will get a good grade !! :)))

@muhammadaalimisaal8453 11 месяцев назад

I am kinda selfish type of person. Usually I donot like videos nor subscribe channels but how precise and to be the point your video was and I'm utterly impressed as this video was helpfull in clearning my concepts about MLR. Goodluck, Best wishes. You have won a subscriber

@richardquinn72 Год назад

Very clear instruction, thanks!

@albertjohnson8605 Год назад

I don't know who you are, but THANK you from deep heart for making this content

@Puputchi 6 месяцев назад

Thank you for the tutorial!

@subhabhadra619 Год назад

Fantastic video.simple to understand

@robinncode 3 месяца назад

Thanks for the amazing insights!

@freeprivatetutor 9 месяцев назад

excellent. very helpful. subscribed!

@tianyouhu5973 Год назад

super helpful, appreciate it

@tejallengare3673 11 месяцев назад

This video is very helpful thank you so much

@KilalibaTugwell Год назад

This video was super helpful

@cientifiko Год назад

thanks... this is awesome

@BayuWicaksana95 Год назад

thank you for the tutorial

@programsolve3053 3 месяца назад

Very well explained 🎉🎉 Thanks you so much 🎉🎉🎉

@seifmostafa4205 Месяц назад

nice video, thanks for your effort ❤

@nobio9591 Год назад

Thanks Dear Rashida

@analyticalmindset Год назад

I would've loved for you to squeak in a Residual analysis or whatever is done after you get your R2 values from your test and train group.

@mistymoose4424 Год назад

omg thank you queen❤

@elijahcota2408 5 месяцев назад

Thank you, god bless

@inamhameed4963 15 дней назад

Great video. Please can you share the insurance data? It's not visible in the description. Thank you

@alirezarahbari3556 Год назад

Helpful🔥

@nevermind9708 7 месяцев назад

i think u can make a function to convert object name into numeric if the the data has many columns instead of writing 1 each 1 like this : for column in df.columns: if not pd.api.types.is_numeric_dtype(df[column]): df[column] = df[column].astype('category') df[column] = df[column].cat.codes df

@regenerativetoday4244 7 месяцев назад

Thank you so much for adding this here. I used this function in some other videos as well.

@RaihanRisad 4 месяца назад

where can i get the dataset that you used

@alirezarahbari3556 Год назад

Nice 👍

@wardaoktoh5060 8 месяцев назад

thank youuuuuuuuuuuuuuuuu miss

@Kennerdoll Год назад

how do i go about passing new values from a user?

@svea3524 Год назад

how do i plotthe fit line over the data?

@christophermiller4637 2 месяца назад

Data isn't my background, but these videos help me understand how to structurally get there. Is there a way to export the predicted charges into a data population for further review. Also, is there a way to adjust the scatter plot dots by a filter on one of the independent variables (i.e. any record where age is 17, make the the plot red color). Thank you!

@richardreneBunalos Год назад

Can you show us how to do OneHotEncoding?

@jayasarojam8568 6 месяцев назад

Great

@raymondkang1329 Год назад

Erm, I think the method you convert the data "region" is inappropriate. U cant convert the "region" as category since it become ordinal data. I think we should convert each of the region into dummy variables then we can see the coefficient of each region.

@SS-st5uv 4 месяца назад

Exactly

@PersonalOne-wn2zd 7 месяцев назад

I have a Different Insight from that i used the Wine data set for that

@Anand-690 19 дней назад

could u plz provide the Dataset being used in the video

@Habbodonald 3 месяца назад

Very good video. About the model, dont you need to check if R-square need an adjust to trust his income?

@regenerativetoday4244 3 месяца назад

There are a few different ways to check the model prediction. R-squared error is one of them. It is common for machine learning models to use mean squared error or mean absolute error as well.

@abbddos Год назад

Good.. but normally we test a model with data that it hasn't seen before, and that's the test split.

@ThobelaGoge 3 месяца назад

How do we access the dataset used?

@shanenicholson94 Год назад

Fantastic video. Very simple and to the point. How can I add the regression line to the chart?

@svea3524 Год назад

do you have the answer?

@shanenicholson94 Год назад

@@svea3524 let me find it later for you. I got it eventually

@sedativelimit 10 месяцев назад

use plt.plot to draw regression line i.e in the format plt.plot(X_train, reg.predict(np.column_stack((X_train))), color='blue', label='Regression Line')

@fatemehrakhshanifar6402 Год назад

Hi, I could find the data but not the code, it's not on your github?

@KilalibaTugwell Год назад

If I developed a model with an r-squared of 0.2. What do I do to improve the performance of the model?

@regenerativetoday4244 Год назад

Try different hyperparameters to improve the model and also different models.

@Essentialenglishwords-ii7ek Год назад

please may i ask you why you didn't put (axis = 1) when you drop a column

@regenerativetoday4244 Год назад

Because it's the default.

@abinanda5754 Год назад

❤

@fariapromi4182 4 месяца назад

Where is the dataset???

@subhasispaul7262 5 месяцев назад

Can you share the following data please

@chiragahlawat465 2 месяца назад

Thank you mam for such a wonderful learning!! I want to know further how can I improve my model accuracy with train score 0.75 and test score -1.12 ??

@regenerativetoday4244 2 месяца назад

First is trying to tune hyperparameters, and also it is normal practice to try different models to find out which model works best for the dataset. Feel free to have a look at this video where you will find a technique for hyperparameter tuning: ru-vid.com/video/%D0%B2%D0%B8%D0%B4%D0%B5%D0%BE-km71sruT9jE.html

@chiragahlawat465 2 месяца назад

@@regenerativetoday4244 Thank you so much you have explained it Amazingly and this video made me very happy! Thank you for this video all the rest!!

@santakmohanty612 8 месяцев назад

Could you also upload or provide a google drive link for the data set file. It would be really helpful.

@regenerativetoday4244 8 месяцев назад

Here is the link to the dataset: github.com/rashida048/Machine-Learning-Tutorials-Scikit-Learn/blob/main/insurance.csv. I am sorry, RU-vid changed their policy for links.

@santakmohanty612 8 месяцев назад

@@regenerativetoday4244 Thanks a lot !!

@manyasachdeva1511 10 месяцев назад

Can you please provide the link for the csv file? I'd like to practice the codes on my own as well

@regenerativetoday4244 10 месяцев назад

Here is the link to the dataset: github.com/rashida048/Machine-Learning-Tutorials-Scikit-Learn/blob/main/insurance.csv Thanks!

@manyasachdeva1511 10 месяцев назад

@@regenerativetoday4244 thank you so much :)

@manyasachdeva1511 10 месяцев назад

Your content is amazing

@mdrahatislamkhan9966 4 месяца назад

x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.2,random_state=0) it works fine but when i swapped the x_train and x_test it gives me error. x_test,x_train,y_train,y_test=train_test_split(x,y,test_size=0.2,random_state=0) why this code gives me error. can you please explain me?

@regenerativetoday4244 4 месяца назад

It should give you error because x_test and y_train have different sizes

@mdrahatislamkhan9966 4 месяца назад

@@regenerativetoday4244i dont got your point. sized are same. I wanted to know if i write x_test,x_train .... it gives me error but it i write x_train,x_test.... then it works fine.

@ceylonroadceylonroad Месяц назад

hi, I'm not able to find your video on improving the R2 score. Can you show me the video? Thanks

@regenerativetoday4244 Месяц назад

You can watch this one that shows how to fine tune hyperparameters that should improve R2 score: ru-vid.com/video/%D0%B2%D0%B8%D0%B4%D0%B5%D0%BE-F13Wbfkpwlw.html

@maishakhatun5635 7 месяцев назад

What if a dataset has columns with numerical values but with symbols, how to do the cleaning?

@maishakhatun5635 7 месяцев назад

I mean comma or currency symbol, thank you

@maishakhatun5635 7 месяцев назад

have you got any videos that calculate the mean absolute error for evaluation?

@JyotirmoyeeRoy 3 месяца назад

Its showing a error as "df isn't defined "

@user-xp2qv2jk7b 3 месяца назад

Please can you send me any link for case study using python polynomial regression (or multi polynomial) with data ? I want to practice.

@regenerativetoday4244 3 месяца назад

Here it is: ru-vid.com/video/%D0%B2%D0%B8%D0%B4%D0%B5%D0%BE-nqNdBlA-j4w.html

@jacintaqiu9919 4 месяца назад

Why my coding shows "TypeError: float() argument must be a string or a real number, not 'Timestamp'"? which one could help me to solve this problem, plz!!

@regenerativetoday4244 4 месяца назад

You need to check the data type of all the columns. If you see any variable is coming as timestamp, that needs to be excluded. Because this tutorial didn't account for datetime datatype. There are different ways of dealing with timestamps. You will find one way of using the timestamp data in this type of models in this tutorial: ru-vid.com/video/%D0%B2%D0%B8%D0%B4%D0%B5%D0%BE-Kt9_AI12qtM.html

@jacintaqiu9919 4 месяца назад

Thank you sooooo much!!!! really helpful:)@@regenerativetoday4244

@sairahulreddykondlapudi8855 Год назад

training and testing on the same dataset?

@user-qc4yk9ko9t 11 месяцев назад

what to do when data have null values?

@regenerativetoday4244 11 месяцев назад

I just added a detailed video on how to deal with null values. Here is the link: ru-vid.com/video/%D0%B2%D0%B8%D0%B4%D0%B5%D0%BE-BnfLUJkrMjs.html

@girlthatcooks4079 7 месяцев назад

On what are you typing your codes this is not vsc?Sorry i am a begginer

@regenerativetoday4244 7 месяцев назад

This is Jupyter Notebook.

@girlthatcooks4079 7 месяцев назад

Thank you so much!

@sheldonoumaotieno6846 Год назад

hey I think the formula and the logic is wrong, though implementation is right. Linear regression even though they may seem it is quite different from the just a simple linear equation. The input features what you define as X are in fact vectors. If you compile n with m training example you have a matrix rather than simple linear equation and it turns out to be a matrix multiplication. The addition is something called bias. The W is the weight. Anyway keep up!

@regenerativetoday4244 Год назад

The bias term in machine leaning term can actually be compared with y_intercept in the linear formula and the weights as coefficients. in y = aX+c, a and X are variables that can be integers, vectors, arrays, or matrices. Same as c. The formula is the concept. I have a detailed tutorial with explanation that shows the linear regression implementation in python from scratch (no libraries), please check if you are interested: regenerativetoday.com/how-to-develop-a-linear-regression-algorithm-from-scratch-in-python/.