Apprentissage supervisé : Création de modèles prédictifs
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Updated
Sep 4, 2023 - Jupyter Notebook
Apprentissage supervisé : Création de modèles prédictifs
ExcelR Data Science Assignment No 3
Multiple Linear Regression Study to predict King County House Sale Prices
Анализ соответствия размера выборки и плановых значений метрик A/B-теста
Predict delivery time using sorting time and Build a prediction model for salary hike.
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.
Created model using Linear regression to predict variables impacting demand.
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)
Exploration of descriptive and inferential statistical methods using Python and Jupyter Notebook.
Explorer, nettoyer et analyser pour effectuer une modélisation des données
ExcelR Data Science Assignment No 1
Add a description, image, and links to the statmodels topic page so that developers can more easily learn about it.
To associate your repository with the statmodels topic, visit your repo's landing page and select "manage topics."