Pushkar's repositories
Gold-Price-forecasting
Gold-Price-forecasting In a personal endevaour to learn about time series analysis and forecasting, I decided to reserach and explore various quantitative forecasting methods.This notebook documents contains the methods that can be applied to forecast gold price and model deployment using streamlit, along with a detailed explaination of the different metrics used to evaluate the forecasts. Goal: The goal of this project was to predict future gold price based on previous gold price. I apply various quantitative methods, (i.e. Times Series Models and Causal Models) to forecast the Price of the gold available in the dataset obtained from Kaggle. Models covered in the Project include: 1.Naive Model 2.ARIMA and Seasonal ARIMA Models 3.Linear Regression Model 4.Model Deployment (Streamlit)
Customer-Behaviour-Pattern
Customer Personality Analysis is a detailed analysis of a company’s ideal customers. It helps a business to better understand its customers and makes it easier for them to modify products according to the specific needs, behaviors, and concerns of different types of customers. Customer personality analysis helps a business to modify its product based on its target customers from different types of customer segments. For example, instead of spending money to market a new product to every customer in the company’s database, a company can analyze which customer segment is most likely to buy the product and then market the product only on that particular segment.
Data-Mining
The objective of this project is to extract textual data articles from URLs and perform text analysis to compute variables.
NLP-TEXT-MINING-SENTIMENT-ANALYSIS-
Perform sentimental analysis on the Elon-musk tweets (Exlon-musk.csv) Extract reviews of any product from ecommerce website like amazon Perform emotion mining
Random-Forest-Project
Random forest is a Supervised Machine Learning Algorithm that is used widely in Classification and Regression problems. It builds decision trees on different samples and takes their majority vote for classification and average in case of regression.