diff --git a/README.MD b/README.MD index ed38256..b0b5861 100644 --- a/README.MD +++ b/README.MD @@ -1,21 +1,24 @@ This repository contains Machine Learning Concepts/Analysis and Algorithms examples. ### Table of Contents -- Analysis-Titanic Dataset -- Analysis-IRIS Dataset -- Analysis-HousePrice Dataset +- Analysis + - Analysis-Titanic Dataset + - Analysis-IRIS Dataset + - Analysis-HousePrice Dataset - Data Wrangling - - Data Cleaning + - Understanding Dataset - Pandas Read From URL - Data Consolidation - - Data Profiling + - Pandas Profiling - MySQL and Pandas - Pivot Tables and GroupBy - Data Visualization -- Feature Selection +- Feature Engineering - ML Concepts - PerformanceMeasure-Classification - Bias-Variance TradeOff + - Curse-Of-Dimentionality + - ML Algorithms - Decision Trees - Support Vector Machines @@ -29,19 +32,19 @@ This repository contains Machine Learning Concepts/Analysis and Algorithms examp
- - ###### [Analysis on Titanic Dataset](https://github.com/rahulvaish/MachineLearning-Python/tree/Titanic) ###### This example is analysis on Titanic Dataset. [[source Code]](https://github.com/rahulvaish/MachineLearning-Python/tree/Titanic) * Load the data[titanic.csv] * Clean and Prepare Data * Create Sparse Matrix - * Split Dataframe as (Train+Test)+(Untouched - Target)
- **Pictorial Representation:**
- ![image](https://user-images.githubusercontent.com/689226/50549588-708d4180-0c85-11e9-8a0e-377368b9fa9c.png) + * Split Dataframe as: + ![image](https://user-images.githubusercontent.com/689226/80577735-01646080-8a25-11ea-8557-a8cb8abeba5f.png) + Representation of Split:
+ ![image](https://user-images.githubusercontent.com/689226/80582594-9b7bd700-8a2c-11ea-85ef-43698b361bb2.png) * Apply ML Model * Make Prediction * Cross Validate - ![image](https://user-images.githubusercontent.com/689226/50550252-f499f600-0c92-11e9-847f-b5c0ef8e7e93.png) + ![image](https://user-images.githubusercontent.com/689226/80578446-168dbf00-8a26-11ea-890a-d3a33ca1d7c3.png) * Make Predictions
@@ -49,12 +52,10 @@ This repository contains Machine Learning Concepts/Analysis and Algorithms examp ###### This example is analysis on IRIS Dataset. [[source Code]](https://github.com/rahulvaish/MachineLearning-Python/tree/IRIS) * Load the data[iris.csv] * Clean and Prepare Data - * Split Dataframe as (Train+Test)+(Untouched - Target)
- **Refer [above](#analysis-on-titanic-dataset) for Pictorial representation** + * Split Dataframe as [above](#analysis-on-titanic-dataset) for Pictorial representation * Apply ML Model * Make Prediction * Cross Validate -![crossvalidation pictorialrepresentation](https://user-images.githubusercontent.com/689226/50550295-ded90080-0c93-11e9-967d-7c00d82a9705.jpg) * Make Predictions
@@ -65,8 +66,7 @@ This repository contains Machine Learning Concepts/Analysis and Algorithms examp * Normalize * Analyse Correlation * Clean and Prepare Data - * Split Dataframe as (Train+Test)+(Untouched - Target)
- **Refer [above](#analysis-on-titanic-dataset) for Pictorial representation** + * Split Dataframe as [above](#analysis-on-titanic-dataset) for Pictorial representation * Apply ML Model * Make Prediction * Cross Validate @@ -74,20 +74,12 @@ This repository contains Machine Learning Concepts/Analysis and Algorithms examp * Make Predictions
-- ###### [Data Cleaning](https://github.com/rahulvaish/MachineLearning-Python/tree/DataWrangling/DataCleaning) - ###### This example explains various methods of cleaning Data. [[Source Code]](https://github.com/rahulvaish/MachineLearning-Python/tree/DataWrangling/DataCleaning) +- ###### [Understanding Dataset](https://github.com/rahulvaish/MachineLearning-Python/blob/DataWrangling/DataCleaning/PandasCrashCourse.ipynb) + ###### This example explains basic computing options with Pandas Dataframe/Series [[Source Code]](https://github.com/rahulvaish/MachineLearning-Python/blob/DataWrangling/DataCleaning/PandasCrashCourse.ipynb) * Load the data[Loan.csv] - * Column selection in Dataframe - * Dropping off Useless Columns - * Dropping off Rows (on condition) - * Type Conversion - * Converting Non-Numerical Columns into Numerical Columns/Sparse - * Determining Null Values - * Filtering Data on Threshold Benchmark - * Handling Missing Values - * Queries on Dataframe - * Split Dataframe Column Values[Example: Date] - * Data Replacment-Unary|Binary|Sparse + * Undersatnding Types + * Column Operations + * Row Operations
@@ -110,7 +102,7 @@ This repository contains Machine Learning Concepts/Analysis and Algorithms examp
-- ###### [Data Profiling](https://github.com/rahulvaish/MachineLearning-Python/tree/DataWrangling/PandasProfiling) +- ###### [Pandas Profiling](https://github.com/rahulvaish/MachineLearning-Python/tree/DataWrangling/PandasProfiling) ###### This example explains how to do Data Profiling using pandas-profiling library. [[Source Code]](https://github.com/rahulvaish/MachineLearning-Python/tree/DataWrangling/PandasProfiling) * Load the Data [iris.csv] * Apply profiling @@ -146,14 +138,11 @@ This repository contains Machine Learning Concepts/Analysis and Algorithms examp
-- ###### [Feature Selection](https://github.com/rahulvaish/MachineLearning-Python/tree/FeatureSelection) - ###### This example explains common methods used for Feature Selection. [[Source Code]](https://github.com/rahulvaish/MachineLearning-Python/tree/FeatureSelection) - * Load the data[IRIS.csv] - * Thresholding on Numerical Features - * Thresholding on Missing Values - * Thresholding on Binary Feature Variance - * Feature Selection from Highly Correlated Features - * Feature Selection on Irrelevant Features for Classification-CHI2|ANOVA +- ###### [Feature Engineering](https://github.com/rahulvaish/MachineLearning-Python/tree/FeatureEngineering) + ###### This example explains common methods used for Feature Engineering. [[Source Code]](https://github.com/rahulvaish/MachineLearning-Python/tree/FeatureEngineering) + * Load the data[mercedesbenz.csv] + * Nominal Encoding (One-Hot Normal | One-Hot KDD | Count/Frequncy) + * Ordinal Encoding (System | Custom)
@@ -170,7 +159,12 @@ This repository contains Machine Learning Concepts/Analysis and Algorithms examp
- ###### [Bias-Variance TradeOff](https://github.com/rahulvaish/MachineLearning-Python/blob/MLConcepts/BiasVarianceTradeOff.md) - ###### This [Sheet](https://github.com/rahulvaish/MachineLearning-Python/blob/MLConcepts/BiasVarianceTradeOff.md) explains the Concepts of Bias-Variance TradeOff. + ###### This [Sheet](https://github.com/rahulvaish/MachineLearning-Python/blob/MLConcepts/BiasVarianceTradeOff.md) explains the concept of Bias-Variance TradeOff. + +
+ +- ###### [Curse-Of-Dimentionality](https://github.com/rahulvaish/MachineLearning-Python/blob/MLConcepts/CurseOfDimentionality.MD) + ###### This [Sheet](https://github.com/rahulvaish/MachineLearning-Python/blob/MLConcepts/CurseOfDimentionality.MD) explains the concept of Curse Of Dimentionality.