https://github.com/jiteshshelke/codsoft
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https://github.com/jiteshshelke/codsoft
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Hello world, this is my profile
- Host: GitHub
- URL: https://github.com/jiteshshelke/codsoft
- Owner: JiteshShelke
- Created: 2024-01-06T13:57:26.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2025-03-30T18:02:58.000Z (about 1 month ago)
- Last Synced: 2025-03-30T18:31:08.108Z (about 1 month ago)
- Language: Jupyter Notebook
- Size: 973 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# 🚀 CodSoft Data Science Projects 🧑💻
## ✨ Author: Jitesh Santosh Shelke
**📌 Batch:** JAN BATCH A26
**📌 Domain:** Data Science
This repository contains three exciting machine learning projects completed as part of CodSoft's Data Science Internship. Each project leverages real-world datasets and showcases various ML techniques. 🌟
---
## 🏆 **TASK-1: Titanic Survival Prediction**
### 🎯 **Objective**
Predict whether a passenger survived the Titanic disaster using machine learning models. 🛳️⚓
### 📂 **Dataset** ([Download Here](https://www.kaggle.com/c/titanic/data))
- `PassengerId`, `Survived`, `Pclass`, `Name`, `Sex`, `Age`, `SibSp`, `Parch`, `Ticket`, `Fare`, `Cabin`, `Embarked`
### 🛠 **Technologies Used**
- 📦 **Libraries:** NumPy, Pandas, Matplotlib, Seaborn, Scikit-learn
- 🤖 **Model:** Logistic Regression
- 📊 **Evaluation Metrics:** Accuracy Score, Confusion Matrix, Classification Report
### 📌 **Implementation**
✅ Data Loading and Preprocessing
✅ Exploratory Data Analysis (EDA)
✅ Model Training and Evaluation
---
## 🎬 **TASK-2: Movie Rating Prediction**
### 🎯 **Objective**
Predict movie ratings based on features such as genre, director, and actors. 🍿🎥
### 📂 **Dataset** ([Download Here](https://www.kaggle.com/datasets/adrianmcmahon/imdb-india-movies))
- `Name`, `Year`, `Duration`, `Genre`, `Rating`, `Votes`, `Director`, `Actor 1`, `Actor 2`, `Actor 3`
### 🛠 **Technologies Used**
- 📦 **Libraries:** Pandas, NumPy, Matplotlib, Seaborn, Scikit-learn
- 🤖 **Models:** Linear Regression, Ridge Regression, Random Forest Regressor, Decision Tree Regressor
- 📊 **Evaluation Metrics:** Mean Squared Error, Mean Absolute Error, R-squared Score
### 📌 **Implementation**
✅ Data Preprocessing and Cleaning
✅ Feature Engineering
✅ Model Training and Hyperparameter Tuning
✅ Model Evaluation
---
## 🌺 **TASK-3: Iris Flower Classification**
### 🎯 **Objective**
Classify Iris flowers into their respective species using machine learning models. 🌿🌸
### 📂 **Dataset** ([Download Here](https://www.kaggle.com/uciml/iris))
- `sepal_length`, `sepal_width`, `petal_length`, `petal_width`, `species`
### 🛠 **Technologies Used**
- 📦 **Libraries:** NumPy, Pandas, Matplotlib, Seaborn, Scikit-learn
- 🤖 **Models:** K-Means Clustering, Gaussian Naïve Bayes
- 📊 **Evaluation Metrics:** Accuracy Score, Confusion Matrix, Classification Report
### 📌 **Implementation**
✅ Data Visualization and Preprocessing
✅ Model Training and Classification
✅ Model Evaluation and Performance Analysis
---
## 💻 **Installation and Usage**
1️⃣ Clone the repository:
```bash
git clone https://github.com/yourusername/codsoft-datascience.git
```
2️⃣ Navigate to the project directory:
```bash
cd codsoft-datascience
```
3️⃣ Install required dependencies:
```bash
pip install -r requirements.txt
```
4️⃣ Run the Jupyter Notebook or Python scripts for each task.
---
## 🤝 **Contact**
For any queries or collaborations, feel free to connect with me:
- 🏆 **GitHub:** (https://github.com/JiteshShelke/Jtxmaster)
- 💼 **LinkedIn:** (https://www.linkedin.com/in/jitesh-shelke-702745286/)
---
**🚀 Made with ❤️ by Jitesh Santosh Shelke 🎯**