Ecosyste.ms: Awesome

An open API service indexing awesome lists of open source software.

Awesome Lists | Featured Topics | Projects

https://github.com/jasonmdev/learning-python-predictive-analytics

Tracking, notes and programming snippets while learning predictive analytics
https://github.com/jasonmdev/learning-python-predictive-analytics

dataset linear-regression logistic-regression predictive-analytics python

Last synced: about 4 hours ago
JSON representation

Tracking, notes and programming snippets while learning predictive analytics

Awesome Lists containing this project

README 

        
# Predictive Analytics with Python



These are my notes from working through the book

[*Learning Predictive Analytics with Python*](https://www.packtpub.com/big-data-and-business-intelligence/learning-predictive-analytics-python)

by [Ashish Kumar](https://in.linkedin.com/in/ashishk64)

and published on Feb 2016.



## General

###Chapter 1: Getting Started with Predictive Modelling

- [x] Installed Anaconda Package.

 - [x] Python3.5 has been installed.

 - [x] Book follows python2, so some codes is modified along the way for python3.



###Chapter 2: Data Cleaning

- [x] Reading the data: variations and examples

- [x] Data frames and delimiters.



####Case 1: Reading a dataset using the read_csv method

- [x] File: titanicReadCSV.py

- [x] File: titanicReadCSV1.py

- [x] File: readCustomerChurn.py

- [x] File: readCustomerChurn2.py

- [x] File: changeDelimiter.py



####Case 2: Reading a dataset using the open method of Python

- [x] File: readDatasetByOpenMethod.py



####Case 3: Reading data from a URL

- [x] Modified the code that it works and prints out line by line dictionary of the dataset.

- [x] File: readURLLib2Iris.py

- [x] File: readURLMedals.py



####Case 4: Miscellaneous cases

- [x] File: readXLS.py

- [x] Created the file above to read from both .xls an .xlsx



####Basics: Summary, dimensions, and structure

- [x] File: basicDataCheck.py

- [x] Created the file above to read from both .xls an .xlsx



####Handling missing values

- [x] File: basicDataCheck.py

- [x] RE: Treating missing data like NaN or None

- [x] Deletion orr imputaion



####Creating dummy variables

- [x] File: basicDataCheck.py

- [x] Split into new variable 'sex_female' and 'sex_male'

- [x] Remove column 'sex'

- [x] Add both dummy column created above.



####Visualizing a dataset by basic plotting

- [x] File: plotData.py

- [x] Figure file: ScatterPlots.jpeg

- [x] Plot Types: Scatterplot, Histograms and boxplots



###Chapter 3: Data Wrangling

####Subsetting a dataset

- [x] Selecting Columns

 - [x] File: subsetDataset.py

- [x] Selecting Rows

 - [x] File: subsetDatasetRows.py

- [x] Selecting a combination of rows and columns

 - [x] File: subsetColRows.py

- [x] Creating new columns

 - [x] File: subsetNewCol.py



####Generating random numbers and their usage

- [x] Various methods for generating random numbers

 - [x] File: generateRandomNumbers.py

- [x] Seeding a random number

 - [x] File: generateRandomNumbers.py

- [x] Generating random numbers following probability distributions

 - [x] File: generateRandomProbDistr.py

 - [x] Probability density function: PDF = Prob(X=x)

 - [x] Cumulative density function: CDF(x) = Prob(X<=x)

 - [x] Uniform distribution: random variables occur with the same (uniform) frequency/probability

 - [x] Normal distribution: Bell Curve and most ubiquitous and versatile probability distribution

- [x] Using the Monte-Carlo simulation to find the value of pi

 - [x] File: calcPi.py

 - [x] Geometry and mathematics behind the calculation of pi

- [x] Generating a dummy data frame

 - [x] File: generateDummyDataFrame.py



####Grouping the data – aggregation, filtering, and transformation

- [x] File: groupData.py

- [x] Grouping

- [x] Aggregation

- [x] Filtering

- [x] Transformation

- [x] Miscellaneous operations



####Random sampling – splitting a dataset in training and testing datasets

- [ ] File: splitDataTrainTest.py

 - [x] Method 1: using the Customer Churn Model

 - [x] Method 2: using sklearn

 - [ ] Method 3: using the shuffle function



####Concatenating and appending data

- [x] File: concatenateAndAppend.py

- [x] File: appendManyFiles.py



####Merging/joining datasets

- [x] File: mergeJoin.py

- [x] Inner Join

- [x] Left Join

- [x] Right Join

- [x] An example of the Inner Join

- [x] An example of the Left Join

- [x] An example of the Right Join

- [x] Summary of Joins in terms of their length



###Chapter 4: Statistical Concepts for Predictive Modelling

####Random sampling and central limit theorem

####Hypothesis testing

- [x] Null versus alternate hypothesis

- [x] Z-statistic and t-statistic

- [x] Confidence intervals, significance levels, and p-values

- [x] Different kinds of hypothesis test

- [x] A step-by-step guide to do a hypothesis test

- [x] An example of a hypothesis test



####Chi-square testing

####Correlation

- [x] File: linearRegression.py

- [x] File: linearRegressionFunction.py

- [x] Picture: TVSalesCorrelationPlot.png

- [x] Picture: RadioSalesCorrelationPlot.png

- [x] Picture: NewspaperSalesCorrelationPlot.png



###Chapter 5: Linear Regression with Python

####Understanding the maths behind linear regression

- [x] Linear regression using simulated data

 - [x] File: linearRegression.py

 - [x] Picture: CurrentVsPredicted1.png

 - [x] Picture: CurrentVsPredictedVsMean1.png

 - [x] Picture: CurrentVsPredictedVsModel1.png



####Making sense of result parameters

- [x] File: linearRegression.py

- [x] p-values

- [x] F-statistics

- [x] Residual Standard Error (RSE)



####Implementing linear regression with Python

- [x] File: linearRegressionSMF.py

- [x] Linear regression using the statsmodel library

- [x] Multiple linear regression

- [x] Multi-collinearity: sub-optimal performance of the model

 - [x] Variance Inflation Factor

  - [x]  It is a method to quantify the rise in the variability of the coefficient estimate of a particular variable because of high correlation between two or more than two predictor variables.



####Model validation

- [x] Training and testing data split 

 - [x] File: linearRegressionSMF.py

- [x] Linear regression with scikit-learn

 - [x] File: linearRegressionSKL.py 

- [x] Feature selection with scikit-learn

 - [x] Recursive Feature Elimination (RFE)

 - [x] File: linearRegressionRFE.py



####Handling other issues in linear regression

- [x] Handling categorical variables

 - [x] File: linearRegressionECom.py

- [x] Transforming a variable to fit non-linear relations

 - [x] File: nonlinearRegression.py

 - [x] Picture:  MPGVSHorsepower.png

 - [x] Picture:  MPGVSHorsepowerVsLine.png

 - [x] Picture:  MPGVSHorsepowerModels.png

- [x] Handling outliers

- [x] Other considerations and assumptions for linear regression



###Chapter 6: Logistic Regression with Python

####Linear regression versus logistic regression

####Understanding the math behind logistic regression

- [x] File: logisticRegression.py

- [x] Contingency tables

- [x] Conditional probability

- [x] Odds ratio

- [x] Moving on to logistic regression from linear regression

- [x] Estimation using the Maximum Likelihood Method

 - [x] Building the logistic regression model from scratch

 - [x] File: logisticRegressionScratch.py

 - [ ] Read above again.

- [x] Making sense of logistic regression parameters

 - [x] Wald test

 - [x] Likelihood Ratio Test statistic

 - [x] Chi-square test

- [x]



####Implementing logistic regression with Python

- [x] File: logisticRegressionImplementation.py

- [x] Processing the data

- [x] Data exploration

- [x] Data visualization

- [x] Creating dummy variables for categorical variables

- [x] Feature selection

- [x] Implementing the model



####Model validation and evaluation

- [x] File: logisticRegressionImplementation.py

- [x] Cross validation



####Model validation

- [x] File: logisticRegressionImplementation.py

- [x] The ROC curve {see terms}



###Chapter 7: Clustering with Python 

####Introduction to clustering – what, why, and how?

- [x] What is clustering?

- [x] How is clustering used?

- [x] Why do we do clustering?



####Mathematics behind clustering

- [x] Distances between two observations

 - [x] Euclidean distance

 - [x] Manhattan distance

 - [x] Minkowski distance

 - [x] The distance matrix

- [x] Normalizing the distances

- [x] Linkage methods

 - [x] Single linkage

 - [x] Compete linkage

 - [x] Average linkage

 - [x] Centroid linkage

 - [x] Ward's method uses ANOVA method

- [x] Hierarchical clustering

- [x] K-means clustering

 - [x] File: kMeanClustering.py



####Implementing clustering using Python

- [x] File: clusterWine.py

- [x] Importing and exploring the dataset

- [x] Normalizing the values in the dataset

- [x] Hierarchical clustering using scikit-learn

- [x] K-Means clustering using scikit-learn

 - [x] Interpreting the cluster



####Fine-tuning the clustering 

- [x] The elbow method

- [x] Silhouette Coefficient



###Chapter 8: Trees and Random Forests with Python

####Introducing decision trees

- [x] A decision tree



####Understanding the mathematics behind decision trees

- [x] Homogeneity

- [x] Entropy

- [x] Information gain

- [x] ID3 algorithm to create a decision tree

- [x] Gini index

- [x] Reduction in Variance

- [x] Pruning a tree 

- [x] Handling a continuous numerical variable

- [x] Handling a missing value of an attribute



####Implementing a decision tree with scikit-learn

- [x] File: decisionTreeIris.py

- [x] Visualizing the tree

- [x] Picture: dtree2.png

- [x] File: dtree2.dot

- [x] Cross-validating and pruning the decision tree



####Understanding and implementing regression trees

- [x] File: regressionTree.py

- [x] Regression tree algorithm

- [x] Implementing a regression tree using Python



####Understanding and implementing random forests

- [x] File: randomForest.py

- [x] The random forest algorithm

- [x] Implementing a random forest using Python

- [x] Why do random forests work?

- [x] Important parameters for random forests



###Chapter 9: Best Practices for Predictive Modelling

####Best practices for coding

- [x] Commenting the codes

- [x] Defining functions for substantial individual tasks

 - [x] Example 1

 - [x] Example 2

 - [x] Example 3

- [x] Avoid hard-coding of variables as much as possible

- [x] Version control

- [x] Using standard libraries, methods, and formulas



####Best practices for data handling



####Best practices for algorithms



####Best practices for statistics



####Best practices for business contexts