IBM Machine Learning Course using Python Repository

Welcome to my IBM Machine Learning Course repository! Here you'll find all the notebooks, datasets, and resources related to the course completed on Coursera. This repository contains a comprehensive collection of machine learning models and exercises covering various topics and techniques.

Notebooks

1. K_means.ipynb: Implementation of K-means clustering algorithm.
2. KKN.ipynb: K-nearest neighbors algorithm.
3. logistic_regression.ipynb: Logistic regression model.
4. multiclass.ipynb: Multiclass classification techniques.
5. multi_regression.ipynb: Multivariate regression analysis.
6. regression_trees.ipynb: Decision trees for regression problems.
7. simple_regression.ipynb: Simple linear regression example.
8. svm.ipynb: Support Vector Machines (SVM) implementation.
9. decision_trees.ipynb: Decision trees algorithm.
10. Taxi_tip_exercise.ipynb: Exercise on predicting taxi tips.
11. credit_card_fraud_detection.ipynb: Fraud detection using machine learning.

Datasets

1. Cell_samples.csv: Dataset for cell samples analysis.
2. ChurnData.csv: Dataset for customer churn analysis.
3. Cust_Segmentation.csv: Customer segmentation dataset.
4. drug200.csv: Dataset for drug effectiveness analysis.
5. Fuelconsumption.csv: Dataset for vehicle fuel consumption analysis.
6. teleCust1000t.csv: Telecom customer dataset.
7. creditcardfraud.csv: Credit card fraud dataset.
8. Yellow_tripdata_2019-06.csv: Yellow taxi trip data for June 2019.

Final Assignment

• Final_Rain_Solved.ipynb: Final assignment solved with screenshots included.

Usage

1. Clone the Repository:

   git clone tps://github.com/mostafa7arafa/MachineLearning_Python_IBM

2. Navigate to the Project Directory:

   cd MachineLearning_Python_IBM

3. Open Jupyter Notebooks: Launch Jupyter Notebook or JupyterLab to explore the notebooks.

   jupyter notebook

   or

   jupyter lab

4. Explore Notebooks: Open the desired notebook from the list and run the cells to understand the implementations and analyses.

5. Utilize Datasets: Access the datasets in the data directory for practicing with the notebooks or for your own analyses.

6. Review Final Assignment: Open the Final_Rain_Solved.ipynb notebook for the solved final assignment with detailed explanations and screenshots.

Certificate

Certificate of completion for the IBM Machine Learning Course on Coursera.