CZ4032-Data-Mining-and-Analytics

Setup virtual environment in project

In Windows CMD, ensure you are in the folder of your repository

1. Run python –m venv venv
2. Run venv\Scripts\activate
3. Run pip install -r requirements.txt

All required packages should have been installed!

venv\Scripts\activate is also the command to enter your virtual environment whenever you want to run the application on CMD

Instruction Guide to read the files

Part 2: Association Rule Mining & Building of Classifier (CBA)

To run: association_rule\association_rule.py and replace the code in lines 12 to 19 (stated below) with respect to the dataset

1) Breast Cancer: "diagnosis" (min_support = 0.05)

DATA_SOURCE = 'BreastCancer\data.csv'

attributes = [
'radius_mean', 'texture_mean', 'perimeter_mean', 'area_mean',
'smoothness_mean', 'compactness_mean', 'concavity_mean',
'concave points_mean', 'symmetry_mean', 'fractal_dimension_mean',
'radius_se', 'texture_se', 'perimeter_se', 'area_se', 'smoothness_se',
'compactness_se', 'concavity_se', 'concave points_se', 'symmetry_se',
'fractal_dimension_se', 'radius_worst', 'texture_worst', 'perimeter_worst',
'area_worst', 'smoothness_worst', 'compactness_worst', 'concavity_worst',
'concave points_worst', 'symmetry_worst', 'fractal_dimension_worst','diagnosis'
]

attribute_to_classify = 'diagnosis'

2) Bank Marketing: "Subscribed" (min_support = 0.3)

DATA_SOURCE = 'BankMarketing\bank.csv'

attributes = [
'age','job','marital','education','default','balance',
'housing','loan','contact','day','month','duration',
'campaign','pdays','previous','poutcome','Subscribed'
]

attribute_to_classify = 'Subscribed'

3) Forest Fire: "Classes" (min_support = 0.1)

DATA_SOURCE = 'ForestFires\Algerian_forest_fires_dataset_UPDATE.csv'

attributes = [
'day','month','year','Temperature','RH','Ws','Rain',
'FFMC','DMC','DC','ISI','BUI','FWI','Classes'  
]

attribute_to_classify = 'Classes'

4) Heart_Failure: "DEATH_EVENT" (min_support = 0.1)

DATA_SOURCE = 'HeartFailure\heart_failure_clinical_records_dataset.csv'

attributes = [
'age','anaemia','creatinine_phosphokinase','diabetes,ejection_fraction',
'high_blood_pressure','platelets','serum_creatinine','serum_sodium','sex',
'smoking','time','DEATH_EVENT'
]

attribute_to_classify = 'DEATH_EVENT'

5) adult_cleaned: "income" (min_support = 0.2)

DATA_SOURCE = 'Adults\data_cleaned.csv'

attributes = [
'age','workclass','fnlwgt','education','education-num',
'marital-status','occupation','relationship','race','sex',
'capital-gain','capital-loss','hours-per-week','native-country',
'income'
]

attribute_to_classify = 'income'

6) Occupancy: "Occupancy" (min_support = 0.05)

DATA_SOURCE = 'Occupancy\data.csv'

attributes = [
'Temperature','Humidity','Light','CO2','HumidityRatio','Occupancy'
]

attribute_to_classify = 'Occupancy'

7) Wine: "Quality_enc" (min_support = 0.05)

DATA_SOURCE = 'Wine\wine.csv'

attributes = [
'fixed_acidity','volatile_acidity','citric_acid',
'residual_sugar','chlorides','free_sulfur_dioxide',
'total_sulfur_dioxide','density','pH','sulphates',
'alcohol','quality'
]

attribute_to_classify = 'quality'

Part 4: Other Classifiers

We have separated 7 datasets into 7 folders named after each respective dataset. To run: Open the classification.ipynb file for your preferred dataset

Part 5: Improvement to CBA

Similar to Step 2, but run: association_rule\association_rule_improved.py instead