Skip to content

Latest commit

 

History

History
74 lines (46 loc) · 3.64 KB

index.md

File metadata and controls

74 lines (46 loc) · 3.64 KB
Raw

Data Science Portfolio

Here are some of my best Data Science Projects. I have explored various machine-learning algorithms for different datasets. Feel free to contact me to learn more about my experience working with these projects.

Examining the adaptibility of online education by students

Skills used: Python, Pandas, SKlearn, Matplotlib

Project Objective: Prediction on Student's Adaptability Level in Online Education.

Quantifiable result: We could predict the Adaptability Level resulting in 89% accuracy.

  • Used Random Forest Regressor to predict the number of bikes rented in the city of Seoul
  • The data had quite a few categorical variables which were encoded for use in the model
  • Encoded categorical variables to numeric using Sklearn due to the presence of many string columns
  • Cross Validation for validating the training data and model fit.
  • Fit a Random Forest Regressor with high prediction accuracy through iteration

The classification goal is to predict if the client will subscribe a term deposit

Skills used: Python, Pandas, SKlearn, Matplotlib

Project Objective: . To explore Bank's Marketing campaign and create meaningful insights from the data

Quantifiable result: We could Classify if the client will subscribe a term deposit resulting in 91.5% accuracy .

  • CatBoost is an open-source gradient boosting library for decision trees developed by Yandex. It is designed to handle categorical data efficiently and is particularly useful for working with datasets that contain a large number of categorical features. Some of the key features of CatBoost include:
  • Encoded categorical variable to numeric
  • Splitted train and test dataset and trained on the data
  • Implemented SMOTE to balance the two class labels and improve the performance

The classification goal is to predict if patient has paget's disease or not based on the orthopedic parameters

Skills used: Python, KNN, NB

Project Objective: To predict normal and abnormal status of patient based on orthopedic parameters using KNN and NB algorithm. Quantifiable result: We could train the data with KNN algorithm to attain a accuracy of [85%] (https://github.com/Ranjan4Kumar/Normal_Abnormal_patient_based_on_Orthopedic_parameters).

  • Prepared the data for training
  • Trained the data with KNN algorithm
  • Trained the data with Naive Bayes algorithm
  • Compared the values for best algorithm

Determining if the clicks of mobile advertisement is fraud or genuine

Skills used: Python, Pandas, SKlearn, Bagging, Boosting

Project Objectives: To predict the probabiltity of click being genuine or fraud based on given features using boosting and bagging technique.

Quantifiable result: We got the accuracy of 99%

  • Applied Random forest model
  • Applied Adaboost ensemble algorithm
  • Applied bagging classifier
  • Apllied gradient boostclassifier model
  • Calculated the accuracy of all the models