https://github.com/atheeralzhrani/data-science-projects

This repository contains my data science projects, where I utilized tools and libraries such as Spark, Python, Pandas, NumPy, SQLite, Matplotlib, Seaborn, and performed Exploratory Data Analysis .
https://github.com/atheeralzhrani/data-science-projects

data-engineering data-preprocessing data-science data-visualization exploratory-data-analysis matplotlib pandas python python-lambda seaborn spark

Last synced: 27 days ago
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This repository contains my data science projects, where I utilized tools and libraries such as Spark, Python, Pandas, NumPy, SQLite, Matplotlib, Seaborn, and performed Exploratory Data Analysis .

• Host: GitHub
• URL: https://github.com/atheeralzhrani/data-science-projects
• Owner: AtheerAlzhrani
• Created: 2024-08-02T08:11:58.000Z (3 months ago)
• Default Branch: main
• Last Pushed: 2024-09-09T07:47:12.000Z (about 2 months ago)
• Last Synced: 2024-09-28T07:01:20.554Z (about 1 month ago)
• Topics: data-engineering, data-preprocessing, data-science, data-visualization, exploratory-data-analysis, matplotlib, pandas, python, python-lambda, seaborn, spark
• Language: Jupyter Notebook
• Homepage:
• Size: 1.53 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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# Data-Science-Projects

This repository contains my data science projects, where I utilized tools and libraries such as Spark, Python, Pandas, NumPy, Matplotlib, Seaborn, and performed Exploratory Data Analysis (EDA) and more, to perform data preprocessing, visualization, and model building.

# Project2

This project provides a comprehensive analysis of the AI-powered job market, including data preprocessing, exploratory data analysis, visualizations, and predictive modeling using a combination of encoded categorical features and scaled salary data. The model's performance was evaluated using RMSE and visualized using a scatter plot. Further steps include finetune the model and exploring additional features to improve prediction accuracy.

# Project1

The project involved analyzing a Telco customer dataset with 21 features on 7043 customers. I used Spark for dataset manipulation and user churn forecasting, with the Naive Bayes model achieving 70.55% accuracy and an F1 score of 0.6741.