Course Name: Applied Data Science Capstone
Platform: Coursera
Instructors: Provided by IBM
This repository contains my work for the Applied Data Science Capstone course offered on Coursera by IBM. The capstone project is the culmination of the Data Science Professional Certificate program and involves solving a real-world data science problem.
The project aimed to predict the success of SpaceX rocket landings. Data was collected from the public SpaceX API and SpaceX Wikipedia page. A 'class' column was created to classify successful landings.
• Data exploration was conducted using SQL, visualization, Folium maps, and dashboards. Relevant columns were selected as features. Categorical variables were converted to binary using one-hot encoding. Data was standardized for consistency.
• Four machine learning models were implemented:
1. Logistic Regression
2.Support Vector Machine
3.Decision Tree Classifier
4.K Nearest Neighbors
All models achieved an accuracy rate of approximately 83.33%.
• The project faced limitations due to data availability. Challenges included addressing data quality issues and achieving a balanced dataset.
- Notebooks: This folder contains Jupyter notebooks for various project stages and analysis.
- Python
- Jupyter Notebook
- Pandas, NumPy, Matplotlib, Seaborn, scikit-learn
- Folium, Plotly
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Clone this repository to your local machine:
git clone https://github.com/ganeshs14/AppliedDSCapstone.git