https://aswath1709.github.io/AswathPortfolio/post/ Recent content in My Works on GoHome Hugo -- gohugo.io en Thu, 02 Mar 2017 12:00:00 -0500 https://aswath1709.github.io/AswathPortfolio/post/project-4/ Mon, 10 Apr 2023 00:00:00 +0000 https://aswath1709.github.io/AswathPortfolio/post/project-4/ Conducted A/B testing to analyze the purchasing behavior of Frequent Shoppers versus Occasional Shoppers, identifying a significant difference in total purchase amounts. Implemented a collaborative filtering recommendation system using k-nearest neighbors, providing personalized product recommendations for customers based on their purchase history. Developed a machine learning model to predict customer churn, utilizing recency, frequency, and monetary value features, achieving an accuracy of [insert accuracy percentage here]% on the test set. Utilized time series forecasting with ARIMA to predict future total purchases, demonstrating the model’s effectiveness in capturing trends and making accurate predictions. Applied k-means clustering to segment customers based on recency, frequency, and monetary values, enabling targeted marketing strategies for different customer groups. https://aswath1709.github.io/AswathPortfolio/post/project-3/ Mon, 10 Apr 2023 00:00:00 +0000 https://aswath1709.github.io/AswathPortfolio/post/project-3/ Extracted and structured around 100k job descriptions by automating scraping on job boards using bs4 and requests, Tokenized job descriptions applying BERT transformer, decreasing corpus size by 30%, and vectorized into matrix with tf-idf Trained and Evaluated Naive Bayes, Gradient Boosting, Random Forest, Support Vector Machine and Logistic Regression to classify job vectors into job titles and attained 88% accuracy with Random Forest Recommended job titles by calculating cosine similarity between classified jobs and user resumes https://aswath1709.github.io/AswathPortfolio/post/project-2/ Mon, 05 Dec 2022 00:00:00 +0000 https://aswath1709.github.io/AswathPortfolio/post/project-2/ Reduced dimensions of a 380-features dataset by 96% through Exploratory Data Analysis (data cleaning, data visualization, feature selection). Built predictive models for homelessness rate with multivariate linear regression and random forest (RMSE 0.35 and 0.18). Github https://aswath1709.github.io/AswathPortfolio/post/project-1/ Sun, 01 May 2022 10:58:08 -0400 https://aswath1709.github.io/AswathPortfolio/post/project-1/ Scraped graduate profiles from Edulix using BeautifulSoup and predprocessed data. Implemented Wilcoxon Rank-Sum p-Test, Generative Additive Models, Kernel Density Estimation to study correlations between “desicion” variable and other variables(continuous) Trained and evaluated SVM, logistic Regression, XGBoost, RandomForest and AdaBoost. Attained the highest AUC-ROC(0.83) and AUC-PR(0.87) with Adaboost and chose it as our final model for admission prediction. Implemented KNN to recommend top ‘k’ universities that best suits with the profile.