Excel to Python Transition: Linear Regression + Python Data Cleaning

From Excel to Python Journey : Linear Regression + Python Data Cleaning #python #excel
Student Performance Prediction in Python - Full Machine Learning Project

Embark on an exhilarating journey with us as we transition "From Excel to Python," unleashing the power of Linear Regression and mastering Python data cleaning along the way! 📈🐍

🔍 Key Highlights:

Excel to Python Transition: Witness the seamless fusion of Excel and Python as we bridge the gap between these powerful tools.
📊 Linear Regression Mastery:

Excel Unleashed: Explore the foundations of Linear Regression in Excel, setting the stage for data insights.
🌐 Python Data Cleaning Power:

Efficient Data Cleanup: Dive into the world of Python and Pandas, discovering the art of data cleaning to prepare your datasets for analysis.
💻 What You'll Learn:

Linear Regression essentials in Excel
Transitioning data skills from Excel to Python
Leveraging Pandas for efficient data cleaning
Practical insights with real-world examples
👩‍💻 Who Should Watch:

Whether you're a data enthusiast, analyst, or aspiring data scientist, this tutorial is tailored for everyone making the transition from Excel to Python.
🚀 Why This Journey Matters:

Bridging the gap between Excel and Python is a game-changer. Enhance your data analysis capabilities and unleash the true potential of both platforms.
🌟 Ready to Begin Your Data Transformation?

Join us on this exciting journey where we navigate the realms of Linear Regression, Excel, and Python data cleaning. Let's empower your data science endeavors! 💼📊 #datascience #linearregression #datacleaning

Kaggle Dataset link - https://www.kaggle.com/datasets/nikhi...

Chapters :-
0:00 Intro
02:30 Kaggle Dataset Multiple Linear regression
03:50 Understand dataset in excel
04:05 Create the Dataframe
06:15 Data Wrangling & Cleaning
08:00 Duplicates & Remove Duplicates Records
17:00 Descriptive Statistics
19:00 Histogram
20:32 Box plot IQR Min Max
23:30 Data shape and spread
25:40 Data Outliers
30:05 Correlation and Heat Map
32:15 Machine Learning Model
32:49 Train & Test Split
34:00 Machine Learning Model Prediction
37:15 Model Accuracy using R2 score
37:50 Compare Actual Versus Predicted Values
38:45 Outro

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