Multiple Linear Regression Study to predict King County House Sale Prices

Apprentissage supervisé : Création de modèles prédictifs

Анализ соответствия размера выборки и плановых значений метрик A/B-теста

Udacity Data Analyst Nanodegree - Project III

Predict delivery time using sorting time and Build a prediction model for salary hike.

Used libraries and functions as follows:

ExcelR Data Science Assignment No 3

Building a classification model for reducing the churn rate for a telecom company.

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.

ExcelR Data Science Assignment No 2

Model created using Logistic Regression to identify potential leads

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

Prepare a prediction model for profit of 50 startups data and Consider only the some columns and prepare a prediction model for predicting Price.

업무프로세스 개선을 위한 관리자의 의사결정 프로그램(현대중공업 DT 프로젝트)

Created model using Linear regression to predict variables impacting demand.

Exploration of descriptive and inferential statistical methods using Python and Jupyter Notebook.

Explorer, nettoyer et analyser pour effectuer une modélisation des données

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.