All Projects → komal11lamba → 50-days-of-Statistics-for-Data-Science

komal11lamba / 50-days-of-Statistics-for-Data-Science

Licence: other
This repository consist of a 50-day program. All the statistics required for the complete understanding of data science will be uploaded in this repository.

Programming Languages

Jupyter Notebook
11667 projects

Projects that are alternatives of or similar to 50-days-of-Statistics-for-Data-Science

featurewiz
Use advanced feature engineering strategies and select best features from your data set with a single line of code.
Stars: ✭ 229 (+1105.26%)
Mutual labels:  feature-selection, feature-extraction, feature-engineering
Market-Mix-Modeling
Market Mix Modelling for an eCommerce firm to estimate the impact of various marketing levers on sales
Stars: ✭ 31 (+63.16%)
Mutual labels:  eda, feature-selection, feature-engineering
feature engine
Feature engineering package with sklearn like functionality
Stars: ✭ 758 (+3889.47%)
Mutual labels:  feature-selection, feature-extraction, feature-engineering
FIFA-2019-Analysis
This is a project based on the FIFA World Cup 2019 and Analyzes the Performance and Efficiency of Teams, Players, Countries and other related things using Data Analysis and Data Visualizations
Stars: ✭ 28 (+47.37%)
Mutual labels:  eda, feature-selection, feature-engineering
exemplary-ml-pipeline
Exemplary, annotated machine learning pipeline for any tabular data problem.
Stars: ✭ 23 (+21.05%)
Mutual labels:  feature-selection, feature-engineering, feature-scaling
dominance-analysis
This package can be used for dominance analysis or Shapley Value Regression for finding relative importance of predictors on given dataset. This library can be used for key driver analysis or marginal resource allocation models.
Stars: ✭ 111 (+484.21%)
Mutual labels:  feature-selection, feature-engineering
Deep Learning Machine Learning Stock
Stock for Deep Learning and Machine Learning
Stars: ✭ 240 (+1163.16%)
Mutual labels:  feature-extraction, feature-engineering
NVTabular
NVTabular is a feature engineering and preprocessing library for tabular data designed to quickly and easily manipulate terabyte scale datasets used to train deep learning based recommender systems.
Stars: ✭ 797 (+4094.74%)
Mutual labels:  feature-selection, feature-engineering
The Building Data Genome Project
A collection of non-residential buildings for performance analysis and algorithm benchmarking
Stars: ✭ 117 (+515.79%)
Mutual labels:  feature-extraction, feature-engineering
pyHSICLasso
Versatile Nonlinear Feature Selection Algorithm for High-dimensional Data
Stars: ✭ 125 (+557.89%)
Mutual labels:  feature-selection, feature-extraction
My Journey In The Data Science World
📢 Ready to learn or review your knowledge!
Stars: ✭ 1,175 (+6084.21%)
Mutual labels:  eda, feature-extraction
Amazing Feature Engineering
Feature engineering is the process of using domain knowledge to extract features from raw data via data mining techniques. These features can be used to improve the performance of machine learning algorithms. Feature engineering can be considered as applied machine learning itself.
Stars: ✭ 218 (+1047.37%)
Mutual labels:  feature-extraction, feature-engineering
Tsfel
An intuitive library to extract features from time series
Stars: ✭ 202 (+963.16%)
Mutual labels:  feature-extraction, feature-engineering
Machine Learning Workflow With Python
This is a comprehensive ML techniques with python: Define the Problem- Specify Inputs & Outputs- Data Collection- Exploratory data analysis -Data Preprocessing- Model Design- Training- Evaluation
Stars: ✭ 157 (+726.32%)
Mutual labels:  feature-extraction, feature-engineering
tsflex
Flexible time series feature extraction & processing
Stars: ✭ 252 (+1226.32%)
Mutual labels:  feature-extraction, feature-engineering
Data-Science
Using Kaggle Data and Real World Data for Data Science and prediction in Python, R, Excel, Power BI, and Tableau.
Stars: ✭ 15 (-21.05%)
Mutual labels:  dimensionality-reduction, feature-engineering
Blurr
Data transformations for the ML era
Stars: ✭ 96 (+405.26%)
Mutual labels:  feature-extraction, feature-engineering
Nni
An open source AutoML toolkit for automate machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter tuning.
Stars: ✭ 10,698 (+56205.26%)
Mutual labels:  feature-extraction, feature-engineering
Complete Life Cycle Of A Data Science Project
Complete-Life-Cycle-of-a-Data-Science-Project
Stars: ✭ 140 (+636.84%)
Mutual labels:  eda, feature-engineering
skrobot
skrobot is a Python module for designing, running and tracking Machine Learning experiments / tasks. It is built on top of scikit-learn framework.
Stars: ✭ 22 (+15.79%)
Mutual labels:  feature-selection, feature-engineering

50-days-of-Statistics-for-Data-Science

This repository consist of a 50-day program. All the statistics required for the complete understanding of data science will be uploaded in this repository.

Sr No Notebook Topic Colab
1 Elements of Structured Data Open In Colab
2 Rectangular Data Open In Colab
3 Estimates of Location Open In Colab
4 Estimates of Variability Open In Colab
5 Exploring the Data Distribution Open In Colab
6 Exploring Binary and Categorical Data Open In Colab
7 Correlation Open In Colab
8 Exploring Two or More Variables Open In Colab
9 Random Sampling and Sample Bias Open In Colab
10 Selection Bias Open In Colab
11 Sampling Distribution of a Statistic Open In Colab
12 The Bootstrap Open In Colab
13 Confidence Intervals Open In Colab
14 Normal Distribution Open In Colab
15 Long-Tailed Distributions Open In Colab
16 Student’s t-Distribution Open In Colab
17 Binomial Distribution Open In Colab
18 Chi-Square Distribution Open In Colab
19 F-Distribution Open In Colab
20 Poisson and Related Distributions Open In Colab
21 A/B Testing Open In Colab
22 Hypothesis Tests Open In Colab
23 Resampling Open In Colab
24 Statistical Significance and p-Values Open In Colab
25 t-Tests Open In Colab
26 Multiple Testing Open In Colab
27 Degrees of Freedom Open In Colab
28 ANOVA Open In Colab
29 Chi-Square Test Open In Colab
30 Multi-Arm Bandit Algorithm Open In Colab
31 Power and Sample Size Open In Colab
32 Simple Linear Regression Open In Colab
33 Multiple Linear Regression Open In Colab
34 Prediction Using Regression Open In Colab
35 Factor Variables in Regression Open In Colab
36 Interpreting the Regression Equation Open In Colab
37 Regression Diagnostics Open In Colab
38 Polynomial and Spline Regression Open In Colab
39 Naïve Bayes Open In Colab
40 Discriminant Analysis Open In Colab
41 Logistic Regression Open In Colab
42 Evaluating Classification Models Open In Colab
43 Strategies for Imbalanced Data Open In Colab
44 K-Nearest Neighbors Open In Colab
45 Tree Models Open In Colab
46 Bagging and the Random Forest Open In Colab
47 Boosting Open In Colab
48 Principal Components Analysis Open In Colab
49 K-Means Clustering Open In Colab
50 Hierarchical Clustering Open In Colab
51 Model-Based Clustering Open In Colab
52 Scaling and Categorical Variables Open In Colab
Note that the project description data, including the texts, logos, images, and/or trademarks, for each open source project belongs to its rightful owner. If you wish to add or remove any projects, please contact us at [email protected].