#### Exercise 1: Linear Regression
**Dataset**: [Linear Regression Dataset](https://www.kaggle.com/kernels/fork-version/34033834)
**Objective**: Predict the target variable using linear regression.
**Tasks**:
1. Load the dataset into a Pandas DataFrame.
2. Perform exploratory data analysis (EDA) to understand the dataset.
3. Split the dataset into training and testing sets.
4. Implement a linear regression model.
5. Evaluate the model using RMSE (Root Mean Square Error).
#### Exercise 2: Classification
**Dataset**: [Breast Cancer Dataset](https://www.kaggle.com/kernels/fork-version/34033834)
**Objective**: Classify whether a tumor is malignant or benign.
**Tasks**:
1. Load the breast cancer dataset.
2. Perform EDA to understand the features and target variable.
3. Implement a logistic regression model.
4. Evaluate the model using accuracy, precision, and recall.
#### Exercise 3: Clustering
**Dataset**: [Iris Dataset](https://www.kaggle.com/datasets)
**Objective**: Cluster the iris flowers based on their features.
**Tasks**:
1. Load the iris dataset.
2. Use K-means clustering to cluster the flowers.
3. Evaluate the clusters using silhouette score.
#### Exercise 4: Ensembling
**Dataset**: [Titanic Dataset](https://www.kaggle.com/datasets)
**Objective**: Predict the survival of passengers on the Titanic.
**Tasks**:
1. Load the Titanic dataset.
2. Implement ensemble methods like Random Forest and Gradient Boosting.
3. Evaluate the ensemble model using accuracy and F1-score.
#### Additional Resources:
- [Kaggle Datasets](https://www.kaggle.com/datasets)
- [Kaggle Competitions](https://www.kaggle.com/competitions)
- [Kaggle Community](https://www.kaggle.com/)
Feel free to dive into these exercises to get hands-on experience with machine learning algorithms. Happy coding! 🤓👩💻👨💻