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Multivariate time series Vector Autoregression Model (VAR) on real world GDP and DPI (and some other indexes). Bayesian Structured Time Series (BSTS).
This repo contains files for the blog post about conjoint analysis
Crop yield Forecasting on the basis of meteorological predictions using some Time series & ML models
My this project repository focused on hypothesis testing involving T-test, Chi-square test, Binomial Test, ANOVA, Sample Size Determination with scipy, statmodels modules.
Using Python to work up a Design of Experiments
Tutorials for BSE classes.
ExcelR Data Science Assignment No 1
ExcelR Data Science Assignment No 2
Forecast the Airlines Passengers and CocaCola Prices data set. Prepare a document for model explaining. How many dummy variables you have created and RMSE value for model. Finally which model you will use for Forecasting.
ExcelR Data Science Assignment No 3
Prepare a prediction model for profit of 50 startups data and Consider only the some columns and prepare a prediction model for predicting Price.
Predict delivery time using sorting time and Build a prediction model for salary hike.
Explorer, nettoyer et analyser pour effectuer une modélisation des données
업무프로세스 개선을 위한 관리자의 의사결정 프로그램(현대중공업 DT 프로젝트)
Apprentissage supervisé : Création de modèles prédictifs
Exploration of descriptive and inferential statistical methods using Python and Jupyter Notebook.
Analysing the results of an A/B test run for an e-commerce website. The company has developed a new web page in order to try and increase the number of users who "convert," meaning the number of users who decide to pay for the company's product.
Tried to understand whether the company should implement a new page or keep the old page with some statistical techniques.
Created model using Linear regression to predict variables impacting demand.
Multiple Linear Regression Study to predict King County House Sale Prices
Time Series forecasting using Seasonal ARIMA. Applied statistical tests like Augmented Dickey–Fuller test to check stationary of series. Checked ACF ,PACF plots. Transformed series to make it stationary
Used libraries and functions as follows:
Udacity Data Analyst Nanodegree - Project III
Building a classification model for reducing the churn rate for a telecom company.
Анализ соответствия размера выборки и плановых значений метрик A/B-теста
Model created using Logistic Regression to identify potential leads