applying Linear Regression (Simple and Multiple), Polynomial Regression, and Logistic Regression on real-world datasets. You will analyze, build, evaluate, and compare models using scikit-learn.
 Regression Models:

Choose a regression dataset (e.g.,student performance).


1.Exploratory Data Analysis (EDA):

•Loaded the data and clean it (handle missing values, encoding, extra if any).

•Visualize the data using appropriate plots (scatter plots, histograms, correlation heatmap).


2.Preprocessing:

•Select appropriate numeric features for regression.

•Scale the features.

•Split the data into training and testing sets (e.g., 80/20).


3.Simple Linear Regression:

•Picked one feature that most correlates with the target variable.

•Fit a simple linear regression model.

•Visualize the regression line and report metrics (R², MSE).


4.Multiple Linear Regression:

•Use multiple features to predict the target.


5.Polynomial Regression:

•Apply Polynomial Regression on the best-performing simple linear feature.

•Try polynomial degrees 2, 3, and 4.

•Scale the polynomial features if needed.

•Compare models using visualizations and metrics.




Logistic Regression with Classification:


Choose a classification dataset (e.g., diabetes prediction)

1.Preprocessing:

•Encode categorical variables and clean the data.

•Scale features if needed.

•Split the dataset into training and testing sets (e.g., 80/20 split).


2.Modeling with Logistic Regression:

•Train a logistic regression model and Predict on your data.


3.Model Evaluation:

•Generate a confusion matrix.

•Visualize the confusion matrix using seaborn or matplotlib.