SarveshChandra/Machine-Learning-and-Artificial-Intelligence-Case-Studies

AIML Projects

AIML Case Studies

Covered fundamental case studies from Statistical analysis, EDA, feature engineering to machine learning and deep learning models..

01. Applied Statistics

Statistical analysis using graphs and distributions

Dataset: Insurance (1000 x 7)

Data preprocessing
    missing value handling
EDA
    5-point summary of numerical attributes
    Univariate analysis
        distplot for distribution for numerical attributes
        stripplot for distribution for categorical attributes
    Bivariate analysis
        pairplot for bi-variate analysis

02. Supervised Learning

Classify using supervised learning models on training dataset and evaluate performances on test dataset

Dataset: users (48k x 15)

Data preprocessing
    concatenate 2 datasets
    missing value handling
EDA
    Univariate analysis
        distplot
        value counts for categorical attributes
    Bivariate analysis
        Pairplot
Feature engineering
    Drop attributes with most values 0, and so not useful in model training
Model training
    Prepare training set and test set
    Train models
        Logistic regression
        Naive Bayes
        KNN classifier
        SVC
            linear kernel
            rbf kernel
            poly kernel
            sigmoid kernel
Performance evaluation on test data
    Model score
    confusion matrix
    Accuracy
        76% (Naive Bayes) to 83% (SVC)
    Precision
        class 0
        class 1
    Recall
        class 0
        class 1

03. Ensemble Techniques

Apply ensemble methods on classification models and evaluate performances

Dataset: bank users (45k x 17)

EDA
    5-point summary of numerical attributes
    Univariate analysis
        distplot
        boxplot
    Bivariate analysis
        pairplot
Data preprocessing
    outlier handling
    Encoding categorical columns
Model training
    Standard classification models
        Decision tree classifier
        Regularized decision tree classifier
        Naive Bayes
    Ensemble methods
        Bagging classifier on regularized decision tree model
        Random forest classifier
        Bagging classifier on Naive bayes model
        Adaboost classifier on Naive bayes model
Performance evaluation on test data
    Confusion matrix
    Accuracy score
        62% to 87%
    Precision
        class 0
        class 1
    Recall
        class 0
        class 1

04. Unsupervised Learning

Perform classification on an unsupervised dataset and evaluate performance

Dataset: vehicles (~1000 x 19)

Data preprocessing
    missing value handling

EDA
    pairplot
    boxplot

Data preprocessing
    z-score on independent attributes to scale the values

Feature engineering
    PCA
        features reduced to 6

Model Training
    SVC model without PCA
    SVC model with PCA

Performance evaluation
    Accuracy score
        92% without PCA
        78% with PCA

5. Featurization, Model selection and Tuning

6. Recommendation Systems

7. Neural Network and Deep Learning

8. Computer Vision - Face Detection

9. Computer Vision - Face Recognition

10. NLP Statistical - Blog authorship corpus

11. NLP - Sentiment Classification

12. NLP - Sarcasm Detection

13. NLP - Automatic Ticket Assignment