Successful Completion of 50 Days in 100 Days of Data Science Code

🚀Celebrating the Successful completion of First 50 Days of Data Science Coding Journey! 🎉📊

Hello, fantastic #LinkedInCommunity! 👋 We've just crossed a significant checkpoint in our #100DaysofDataScienceCode, and I couldn't be more excited to share our remarkable achievements with you. 🌟📈🔥

🔹 Celebrating 50 Days of Data Science Coding Challenge 🔹

We've come a long way, and today, on Day 50, we're not just celebrating a number; we're celebrating our dedication, learning, and growth. We've tackled complex concepts and applied them to real-world projects. 🌐💼

🌟 Achievements So Far:

In the first 50 days, we've delved into various aspects of Data Science, including:

Classification Algorithms:

• 📈 Logistic Regression
• 🌳 Decision Tree
• 🌲 Random Forest
• 🎯 Support Vector Machines (SVM)
• 🧵 K-Nearest Neighbors (K-NN)

Regression Algorithms:

• 📊 Linear Regression
• 📈 Polynomial Regression
• 🌳 Decision Tree Regression
• 🌲 Random Forest Regression
• 🎯 Support Vector Regression (SVR)
• 🧵 K-Nearest Neighbors (K-NN) Regression

🌟 Capstone Projects:

We've not just learned these algorithms but also implemented them in two exciting Capstone Projects:

1. Gender Classification Project:

• 🧹 Data Pre-Processing : 1. Handle Missing Data, 2. Handle Duplicates, 3. Handle Categorical Data, 4. Handle Outliers (Drop, Transform), 5. Visualize the data, 6. Train test split
• 🛠️ Implementation of Logistic Regression, Decision Tree, Random Forest, SVM, and KNN
• 🧾 Model Evaluation :1. Accuracy Score, 2. Confusion Matrix, 3. Classification Report
• Link to the Kaggle Notebook : https://www.kaggle.com/code/snehalsanjaymankar/gender-classification-lr-dt-rf-svm-and-knn
• 📊 Comparison of Model Performances (Bar Chart)

2. Resource Allocation in 5G Network Service Project:

• 🧹 Data Pre-Processing : 1. Handle Missing Data, 2. Handle Duplicates, 3. Handle Categorical Data, 4. Handle Outliers (Drop, Transform), 5. Visualize the data, 6. Train test split
• 🧮 Handling Outliers :1. Removing Outliers, 2. Transforming Values
• 🛠️ Implementation of Polynomial Regression, SVM Regression, and KNN Regression
• 🧾 Model Evaluation :1. Mean Absolute Errors, 2. Mean Square Errors, 3. Root Mean Square Errors
• Link to the Kaggle Notebook : https://www.kaggle.com/code/snehalsanjaymankar/5g-quality-of-service-mlr-svr-and-knn-regr
• 📊 Comparison of Model Performances (Multiple Bar Charts)

🌟 Learning and Growth:

Our journey is not just about reaching milestones; it's about learning, growing, and continuously pushing boundaries. Every algorithm, every project, and every line of code has contributed to our knowledge and expertise in the world of Data Science.

🌐 GitHub Repository:

All our code, notebooks, and project materials are available in our GitHub repository, where you can explore our progress, download resources, and see how we've evolved during these 50 days.

🔗 GitHub Repository Link:

🔗 All Handwritten Notes GitHub Repository Link:

🌟 What's Next?

As we embark on the next 50 days of this incredible journey, we're set to explore more advanced topics, tackle complex challenges and continue our pursuit of Data Science excellence. Stay tuned for more updates and exciting projects ahead! 🚀🌐📈

#100DaysofDataScienceCode #100DaysOfDataScience #DataScience #MachineLearning #DataScienceProjects #LinkedInPost