https://github.com/yashodatta15/project_portfolio
https://github.com/yashodatta15/project_portfolio
data-analysis data-science data-visualization python sql
Last synced: about 1 year ago
JSON representation
- Host: GitHub
- URL: https://github.com/yashodatta15/project_portfolio
- Owner: Yashodatta15
- Created: 2023-06-07T16:43:47.000Z (about 3 years ago)
- Default Branch: main
- Last Pushed: 2025-03-07T05:01:15.000Z (over 1 year ago)
- Last Synced: 2025-03-07T05:28:19.448Z (over 1 year ago)
- Topics: data-analysis, data-science, data-visualization, python, sql
- Homepage:
- Size: 125 KB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# 🗺 Yashodatta's Portfolio
Welcome to my data portfolio! Here, I document a summary of my projects in the data field.
## 📚 Table of Contents
- [SQL](#sql)
- [Machine/ Deep Learning](#machine-Deep-learning)
- [Gen-AI](#Gen-AI)
- [Tableau and PowerBi](#tableau-and-powerbi)
- [Python](#python)
# SQL
| Project Link | Area of Analysis | Project Description |
|---|---|---|
| :truck: [Logistic company analysis](https://github.com/Yashodatta15/SQL_Project_on_Logistic_Company) | Data analysis | A SQL project on a logistic company would involve creating a database to manage various aspects of the company's operations, such as tracking shipments, managing inventory, and handling customer orders. |
|🛵🍛[Swiggy Project](https://github.com/Yashodatta15/Swiggy_Project) | Data analysis | My project on Swiggy analysis in SQL explores consumer preferences and order trends within the food delivery platform.|
***
# Machine/ Deep Learning
| Project Link | Area | Project Description | Model | Libraries |
|---|---|---|---|---|
| :factory: [Metal casting image](https://github.com/Yashodatta15/Metal-casting-product-image-classification-for-quality-inspection) | Deep Learning | The objective of this project is to automate the process of finding defects in the casting process. |CNN, Logistic Regression| Keras, Layers, Sequential, Matplotlib |
| :rock: :bomb: [Sonar Rock and Mine Predication](https://github.com/Yashodatta15/SONAR-ROCK-AND-MINE-PREDICTION) | Data Analysis, Supervised Learning | The objective of this project is to create an advanced machine learning system capable of accurately distinguishing between rocks and mines in submarine environments, with the aim of enhancing submarine safety by minimizing the occurrence of misidentifications and potential accidents. | Logistic Regression, Dicision Tree, Random forest | Pandas, Numpy, Scikit-Learn |
| :books: [Book recommendations system](https://github.com/Yashodatta15/Book_Recommendations_System) | Supervised Learining | The Book Recommendation System with Cosine Similarity suggests personalized book recommendations based on user preferences, using cosine similarity to measure book feature similarity. | cosine similarity | Pandas, Numpy, Scikit-Learn |
| 💗 [Heart disease project](https://github.com/Yashodatta15/Heart_disease_project) | Data Analysis, Supervised Learning | A machine learning model for heart disease prediction using patient data. | Logistic Regression, DecisionTreeClassifier, RandomForestClassifier, KNeighborsClassifier, SVC | Pandas, Numpy, Scikit-Learn, Matplotlib, Seaborn |
|🎐 [Maintenance Cost Minimization of Machinery for Wind Energy Production](https://github.com/Yashodatta15/Maintenance-Cost-Minimization-of-Machinery-for-Wind-Energy-Production-Using-Machine-Learning) | Data Cleaning, Data Analysis, EDA, Supervised Learning | ML-based solution predicts wind turbine machine failure ahead of time for a renewable energy company, maximizing recall to minimize expensive replacements. Final model is XGBoost Classifier trained on original data | LogisticRegression, DecisionTreeClassifier, RandomForestClassifier, AdaBoostClassifier, GradientBoostingClassifier, BaggingClassifier | Pandas, Numpy, Scikit-Learn, Matplotlib, Seaborn |
|💹[Stock Price Predication(PSYLIQ internship)](https://github.com/Yashodatta15/Stock-price-predication-) | Time Series |This project employs machine leraning, specifically LSTM network, to predict stock price using the NSE-TATA GLOBAL dataset. The code encompasses data preprocessing, model training, evaluation, and visualization, offeringg a comprehensive approch to time series forecasting in finance. |LSTM |Pandas, Numpy, Matplotlib, Keras |
|✍️[Handwritten Digit Recognition(PSYLIQ internship)](https://github.com/Yashodatta15/Handwritten-Digit-recognition) |Deep Learning|This project focuses on Handwritten Digit Recognition using a Neural Network, employing the MNIST dataset. By leveraging the power of machine learning, we aim to unravel the complexities of digit classification and offer insights into the practical implementation of image recognition systems.|Neural Network|Pandas, Numpy, Matplotlib, Tensorflow, CV2 |
|😀😔[Sentiment Analysis Using LSTM](https://github.com/Yashodatta15/Sentiment-Analysis-Using-LSTM) | NLP | This project utilizes LSTM networks for sentiment analysis, a type of RNN designed to capture dependencies over time. By training on large datasets of text with associated sentiment labels, the LSTM model learns to classify text into positive, negative, or neutral sentiment categories with high accuracy. | LSTM | Tensorflow, keras |
***
# Gen-AI
| Project Link | Area | Project Description | Tool |
|---|---|---|---|
| 📧[Personal_cold_email_generator](https://github.com/Yashodatta15/Personal_cold_email_generator) | Gen-AI | The 'Personal Cold Email Generator' is a project that creates personalized and effective cold emails using AI and advanced text processing techniques.| streamlit, llm, langchain, chromadb |
***
# Tableau and PowerBi
| Project Link | Project Description | Project Link | Tool |
|---|---|---|---|
| 🦄 [Maven Unicorn Challenge](https://github.com/Yashodatta15/Maven_Unicorn_Challenge) | Cleansed and transformed data on privately-owned companies (start-ups) valued at over $1 billion using Python. Visualised key insights using Tableau, including the timeline of valuations, the top 10 countries and investors with the highest valuations, the most successful unicorns, and the average time it takes to reach a $1 billion valuation. | [Link](https://github.com/Yashodatta15/Maven_Unicorn_Challenge) | PowerBI |
|:bank: [FINANCIAL COMPALINTS](https://github.com/Yashodatta15/FINANCIAL-COMPALINTS) | It is critical to analyses customer complaint data to determine the root reasons of consumer unhappiness and make necessary modifications. By analyzing, how intelligent computing may be utilized to better understand and enhance public services through analysis using Tableau Interactive Dashboard. | [Link](https://github.com/Yashodatta15/FINANCIAL-COMPLAINTS) | Tableau |
|💊 [Pharma Sales Dashboard(PSYLIQ internship)](https://github.com/Yashodatta15/Pharma-Sales-Dashboard) |Spanning from 2017 to 2022, this dashboard unravels insights into a pharma company across diverse dimensions. It boosts a user-friendly Parametric range,allowing users to customize their Top Products. |[Link](https://github.com/Yashodatta15/Pharma-Sales-Dashboard)|PowerBI|
|🔫 [Terrorism Dashboard(INTERN internship)](https://github.com/Yashodatta15/Terrorism-Dashboard) | Developed an interactive global terrorism dashboard using Power BI. Provided valuable insights into terrorism trends and patterns. Supported informed decision-making to mitigate the impact of terrorism worldwide. | [Link](https://github.com/Yashodatta15/Terrorism-Dashboard) | PowerBI |
|🎼 [Spotify Dashborad](https://github.com/Yashodatta15/Spotify_Dashborad) | Crafting a dynamic Spotify dashboard in Power BI to visualize music trends and user engagement. | [Link](https://github.com/Yashodatta15/Spotify_Dashborad) | PowerBI |
***
# Python
| Project Link | Area | Project Description | Libraries |
|---|---|---|---|
| 📺 [Top 250 anime2023 Analysis](https://github.com/Yashodatta15/Top-250-anime-2023-EDA) | Data Cleaning, Data Analysis, EDA | In this project I builed various plot to analyze the data | pandas, Numpy, Matplotlib, Seaborn |
| 🎮 [Rock-Paper-Scissor Game](https://github.com/Yashodatta15/Rock-Paper-Scissor-Game) | Programming | Python implementation of Rock, Paper, Scissors game where users play against the computer for 5 rounds, with score tracking and winner declaration. Users input their choice (r/p/s) and results of each round are displayed, concluding with the overall winner. | - |
|🎥 [Youtube Streamer Analysis(INTERN internship)](https://github.com/Yashodatta15/Youtube-Streamer-Analysis) | Data Cleaning, Data Analysis, EDA | The initial exploration of the YouTube dataset revealed valuable insights into the structure and characteristics of the top 1000 YouTubers. Key observations include identifying trends in content categories, exploring audience distribution by country, and analyzing performance metrics. | Pandas, Numpy, Matplotlib, Seaborn |
***