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https://github.com/srinibas-masanta/ibm-applied-data-science-capstone

This repository contains the work completed for the Applied Data Science Capstone Project offered by IBM on Coursera. The capstone project is the final course in the IBM Data Science Professional Certificate series and serves as an opportunity to apply the skills and knowledge gained throughout the series to a real-world data science problem.
https://github.com/srinibas-masanta/ibm-applied-data-science-capstone

capstone-project data-analysis data-science data-visualization machine-learning python web-scraping

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This repository contains the work completed for the Applied Data Science Capstone Project offered by IBM on Coursera. The capstone project is the final course in the IBM Data Science Professional Certificate series and serves as an opportunity to apply the skills and knowledge gained throughout the series to a real-world data science problem.

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README 

        
# Applied Data Science Capstone Project



## Overview

Welcome to the Applied Data Science Capstone Project, where we embark on a journey to predict the successful landing of the Falcon 9 first stage. SpaceX, known for revolutionizing space travel, advertises Falcon 9 rocket launches at a competitive cost of 62 million dollars, compared to other providers' costs upwards of 165 million dollars. This significant cost-saving is largely attributed to SpaceX's ability to reuse the first stage of the rocket. By accurately predicting the landing success of the first stage, we can better estimate the launch costs, providing valuable insights for companies bidding against SpaceX for rocket launches.



## Objectives

The project is structured into several comprehensive modules, each designed to build on the previous one, culminating in a robust predictive model. Below is a detailed description of the tasks and goals achieved in each module:



### 1. Request to the SpaceX API and Data Wrangling

- **Data Collection**: We initiated our project by making a GET request to the SpaceX API to gather historical launch data. This data included various parameters essential for our analysis.

- **Data Cleaning**: After obtaining the data, we performed cleaning and formatting to ensure consistency, removing any anomalies or missing values, and preparing it for analysis.



### 2. Web Scraping Falcon 9 Launch Records

- **Web Scraping with BeautifulSoup**: We extracted Falcon 9 launch records from Wikipedia using BeautifulSoup, a powerful Python library for web scraping.

- **Data Parsing**: The extracted HTML table was parsed and converted into a Pandas DataFrame, facilitating easy manipulation and analysis of the data.



### 3. Exploratory Data Analysis (EDA) and Training Labels

- **Exploratory Data Analysis**: We conducted extensive EDA using visualization tools like Matplotlib and Seaborn to uncover patterns and correlations in the data.

- **Training Labels**: Identified and labeled the training data, crucial for the subsequent machine learning models.



### 4. Database Integration

- **Loading Data into Db2**: We loaded the dataset into a Db2 database to leverage SQL for structured querying and analysis.

- **Executing SQL Queries**: Executed various SQL queries to derive insights and answer specific questions related to the launch data.



### 5. Feature Engineering and Visualization

- **Feature Engineering**: Created new features from the existing data to enhance the predictive power of our models.

- **Visualization with Folium**: Used Folium to create interactive maps, marking launch sites and visualizing success and failure rates, helping to identify geographical patterns.



### 6. Interactive Visual Analytics with Plotly Dash

- **Building the Dash Application**: Developed an interactive Plotly Dash application, allowing users to perform real-time visual analytics on the SpaceX launch data.

- **User Interaction Components**: Incorporated dropdown lists and range sliders to enable dynamic interaction with pie charts and scatter plots, making the analysis more intuitive and engaging.



### 7. Machine Learning Models and Hyperparameter Tuning

- **Data Standardization and Splitting**: Standardized the dataset and split it into training and test sets to ensure robust model evaluation.

- **Hyperparameter Tuning**: Performed hyperparameter tuning using GridSearchCV for various models, including SVM, Classification Trees, and Logistic Regression.

- **Model Evaluation**: Evaluated each model's performance on the test data to identify the best-performing model.



## Results

- **Decision Tree Classifier**: Achieved the highest accuracy of 0.9444 on the test set, making it the best-performing model.

- **SVM and K-Nearest Neighbors**: Both models attained an accuracy of 0.8333 on the test set.



## Conclusion

Through meticulous data analysis, feature engineering, and model tuning, this project successfully predicted the landing success of the Falcon 9 first stage. The insights gained are invaluable for estimating launch costs and aiding companies in their competitive bids against SpaceX.



## Repository Structure

- **data/**: Contains the dataset and any data-related scripts.

- **notebooks/**: Jupyter notebooks documenting each module of the project.

- **scripts/**: Python scripts for data processing, visualization, and modeling.

- **dash_app/**: Code for the Plotly Dash interactive application.

- **README.md**: Comprehensive project overview and detailed description.



## Acknowledgments

- **IBM**: For providing the course and learning materials.

- **Coursera**: For the platform to access and complete the course.



Explore this repository to dive into the complete workflow, methodologies, and innovative techniques employed in this capstone project. Whether you're a data science enthusiast, a space exploration aficionado, or a professional seeking insights into predictive modeling, this project offers something for everyone.