Simple Linear Regression Model in Python with scikit-learn
Learn how to solve a linear regression problem to predict mobile phone prices using Python in Jupyter Notebook. This tutorial covers the entire workflow, including exploratory data analysis, data preprocessing, model building, and evaluation. You will also learn how to compare different machine learning algorithms to find the best model for price prediction.
Follow along as we import necessary libraries, analyze and visualize the dataset, handle missing values, and scale features. The video demonstrates building and evaluating multiple regression models, including linear regression, ridge, lasso, decision tree, random forest, and gradient boosting, to determine which approach yields the most accurate results.
00:00 Introduction
00:17 Overview of the Problem
00:32 Required Libraries
01:28 Dataset Description
02:31 Loading the Data
03:11 Exploring Data Structure
03:56 Checking for Missing Values
04:48 Summary Statistics
05:38 Correlation Analysis
06:18 Visualizing Correlations
07:03 Pair Plot Visualization
08:46 Data Exploration Insights
09:12 Data Preprocessing Steps
10:44 Feature Scaling
11:53 Building the Linear Regression Model
13:19 Model Evaluation Metrics
15:01 Interpreting Results
15:30 Exploring Alternative Models
16:20 Initializing Multiple Models
17:01 Evaluating All Models
19:06 Comparing Model Performance
20:26 Selecting the Best Model
20:55 Conclusion and Next Steps
#MachineLearning #Regression #DataScience