Some Projects I’ve Worked On…

Car Price Prediction(Linear Regression)

In the Car Price Prediction Project, I used Python to predict car selling prices based on various attributes including the year of manufacture, current price, and kilometers driven. After processing and splitting the data into training and testing sets, a Linear Regression model was applied. The model effectively predicted car prices, aiding both potential buyers and sellers in making informed decisions, and showcased its efficiency with a high R² score.

Credit scoring and Segmentation (k-means Clustering)

The Credit Scoring and Segmentation System, developed using the k-means clustering algorithm in Python, evaluates the creditworthiness of individuals or businesses. This system segments borrowers into distinct groups based on their credit profiles.

Sales Prediction(Time Series)

This model employs advanced time series forecasting techniques in Python to predict champagne sales. It begins by analyzing the non-stationarity of the historical sales data, using the Dickey-Fuller test to confirm its presence. To uncover underlying patterns and temporal dependencies, the model makes use of the Auto-Correlation Function (ACF) and the Partial Correlation Function (PCF). Once the data is transformed into a stationary format, the data is trained to project future sales Using ARIMA and SARIMA models

Recommendation System (Restaurant Recommendation)

The restaurant recommendation system, developed in Python, is tailored to steer customers to restaurants that best match their tastes. Utilizing a rich dataset sourced from Kaggle, the system places significant emphasis on the ‘type’ of the restaurant, which represents its category. By analyzing and matching these categories with a customer’s tastes, the system efficiently suggests similar restaurant options, enhancing the dining selection experience for users.

Sentiment Analysis (Flipkart Reviews)

In this project, I conducted a sentiment analysis on Flipkart reviews using Python. By examining the feedback from customers, the analysis determines the prevailing sentiments – be it positive, negative, or neutral. Through this detailed analysis, we gain a deeper understanding of customer satisfaction and areas of improvement, providing valuable insights for enhancing the overall shopping experience on Flipkart.

Time Series Forecasting (Netflix Subscription)

The Netflix Subscription Project, executed in Python, utilizes the ARIMA time series forecasting model to predict subscription trends. By analyzing historical subscription data, the model discerns patterns and provides projections, offering insights into potential future growth and fluctuations for the streaming service.

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