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https://github.com/rubydamodar/codsoft-ds-projects
I'm completing five comprehensive tasks in the CODSOFT Data Science internship: Titanic Survival Prediction, Movie Rating Prediction, Iris Flower Classification, Handwritten Digit Recognition, and Stock Price Forecasting.
https://github.com/rubydamodar/codsoft-ds-projects
Last synced: about 1 month ago
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I'm completing five comprehensive tasks in the CODSOFT Data Science internship: Titanic Survival Prediction, Movie Rating Prediction, Iris Flower Classification, Handwritten Digit Recognition, and Stock Price Forecasting.
- Host: GitHub
- URL: https://github.com/rubydamodar/codsoft-ds-projects
- Owner: rubydamodar
- Created: 2024-11-11T09:21:45.000Z (about 1 month ago)
- Default Branch: main
- Last Pushed: 2024-11-11T09:54:11.000Z (about 1 month ago)
- Last Synced: 2024-11-11T10:32:33.471Z (about 1 month ago)
- Language: Jupyter Notebook
- Size: 3.44 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# 🌟 CODSOFT Data Science Internship🌟
Welcome to my GitHub repository for the **CODSOFT Data Science Internship**! I’m thrilled to share my journey as a BCA (Data Science) student at Amity University, showcasing the projects I completed during this enriching experience. This repository highlights my hands-on work, skills development, and the growth I achieved as an aspiring data scientist.
## ✨ About CODSOFT
CODSOFT is a dynamic and inspiring community that brings together passionate individuals aiming to develop their skills and foster leadership. Through engaging mentorship programs, workshops, and collaborative projects, CODSOFT empowers learners to excel and make impactful contributions in their chosen fields. 🌱
## 📂 Projects and Tasks
### 1. 🚢 Titanic Survival Prediction
**Objective**: Predict whether a passenger on the Titanic survived or not.
**Dataset**: Includes data on age, gender, ticket class, fare, and more.
**What I Learned**:
- 📊 **Data Cleaning & Preprocessing**: Handling missing data and preparing it for analysis.
- 🔍 **Exploratory Data Analysis (EDA)**: Gaining insights through visualization.
- ⚙️ **Modeling**: Building and fine-tuning classification algorithms.
- 📈 **Evaluation**: Assessing model performance with accuracy and other metrics.
### 2. 🎬 Movie Rating Prediction
**Objective**: Predict movie ratings based on features like genre, director, and actors.
**What I Learned**:
- 🛠 **Feature Engineering**: Selecting and creating relevant features.
- 📈 **Regression Techniques**: Implementing and tuning regression models.
- 📊 **Data Visualization**: Using visual tools to understand relationships and patterns.
- ✅ **Model Validation**: Ensuring reliable and accurate predictions.
### 3. 🌼 Iris Flower Classification
**Objective**: Classify the Iris species (setosa, versicolor, virginica) based on petal and sepal measurements.
**What I Learned**:
- 📊 **Data Visualization**: Visualizing distributions and feature relationships.
- 🤖 **Model Training**: Applying and comparing classification algorithms.
- 🧪 **Evaluation Metrics**: Using accuracy and confusion matrices to measure model success.
### 4. ✍️ Handwritten Digit Recognition
**Objective**: Recognize handwritten digits from the MNIST dataset.
**What I Learned**:
- 🖥️ **Deep Learning**: Building neural networks with frameworks like TensorFlow/Keras.
- 🔧 **Data Augmentation**: Enhancing training data for better model performance.
- 📊 **Model Assessment**: Evaluating precision, recall, and overall accuracy.
### 5. 📈 Stock Price Forecasting
**Objective**: Forecast stock prices using historical data.
**What I Learned**:
- ⏳ **Time Series Analysis**: Understanding trends, seasonality, and patterns in data.
- 🔄 **Predictive Models**: Implementing ARIMA and LSTM models for forecasting.
- 📉 **Model Evaluation**: Measuring performance with RMSE and other error metrics.
## 🏆 Skills I Developed
- **Data Cleaning & Preprocessing** 🧼
- **Feature Engineering** 🛠
- **Exploratory Data Analysis** 📊
- **Machine Learning & Deep Learning** 🤖
- **Model Validation & Evaluation** 📈
## 🌟 Conclusion
Completing these projects has significantly strengthened my understanding of data science principles, machine learning algorithms, and practical problem-solving skills. I am proud to have applied these learnings in a real-world context and grown as a data science professional.
Thank you for exploring my work!
**Ruby Poddar**
*BCA (Data Science), Amity University* 🌟