1) Implemented K-Means clustering on description, genre, and cast text to categorize 7.7K TV shows and movies into 5 different clusters which helped in garnering insights on Netflix and how content is being consumed.2) Implemented text preprocessing techniques such as text cleaning, stemming and performed vectorization of textual data using TF IDF followed by dimensionality reduction using PCA.3) Implemented K-Means Clustering and evaluated the optimal clusters using the Silhouette score and Elbow method. 4) Created content-based recommender system using count vectorizer and cosine similarity to find the top 10 recommended similar movies or TV shows for the provided movie name.

1)Achieved an F1 score of 0.79 by applying a sentiment classification model on tweetsfrom the targeted population to predict their morale index. 2)Executed essential text pre-processing by using methods such as stop word removal, stemming, and named entity recognition (NER) to clean and vectorize raw text. 3)Generated optimal document-word sparse matrix using TF-IDF Vectorizer and implemented a Naïve Bayes model. Deployed the sentiment model on Heroku with the aid of Flask API where one can feed tweets to check its sentiment.

1) Built a regression model using Linear Regression to predict the weekly sales volume of 10K+ Rossman drug stores for efficient inventory management. 2)Achieved an R squared score of 95.5% on the test dataset by applying XGBoostregularization for feature selection, and used RandomizedSearchCV for hyperparameter tuning3)Employed processing techniques such as outlier treatment using IQR, multicollinearity check using VIF, feature scaling using MinMaxScaler

1) To examine patterns and trends in hotel reservations to gain insights into factors that impact the hospitality industry, such as customer preferences, seasonal fluctuations, and economic condition. 2 ) Cleaned, and pre-processed the dataset using Python, and created an interactive dashboard on Tableau.3) Performed Univariate, Bivariate, and multivariate analysis using NumPy and Pandas and visualized with Matplotlib, and Seaborn using Python.4) Presented valuable findings and insights into the performance of the hospitality industry including occupancy rates, revenue ,and pricing, to inform decision-making and improve business strategies.