Usage

Example 1

# Import Libraries
import numpy as np
import pandas as pd
from sklearn.linear_model import LogisticRegression
from sklearn.ensemble import RandomForestClassifier, DecisionTreeClassifier
from sklearn.snm import SVC
from buildml import SupervisedLearning

# Get Dataset
dataset = pd.read_csv("Your_file_path")  # Load your dataset(e.g Pandas DataFrame)
data = SupervisedLearning(dataset)

# Exploratory Data Analysis
eda = data.eda()
eda_visual = data.eda_visual()

# Build and Evaluate Classifier
classifiers = [
    "LogisticRegression(random_state = 0)",
    "RandomForestClassifier(random_state = 0)",
    "DecisionTreeClassifier(random_state = 0)",
    "SVC()"
    ]
build_model = data.build_multiple_classifiers(classifiers,
                                          kfold=5,
                                          cross_validation=True,
                                          graph=True,
                                          length=8,
                                          width=12)

Example 2: Working on a dataset with train and test data given.

# Import Libraries
import pandas as pd
import numpy as np
from sklearn.linear_model import LogisticRegression
from sklearn.svm import SVC
from sklearn.ensemble import RandomForestClassifier
from sklearn.tree import DecisionTreeClassifier
from xgboost import XGBClassifier
from buildml import SupervisedLearning

# Get Dataset
training_data = pd.read_csv("train.csv")
test_data = pd.read_csv("test.csv")

dataset = pd.concat([training_data, test_data], axis = 0)

# BuildML on Dataset
automate_training = SupervisedLearning(training_data)
automate_test = SupervisedLearning(test_data)

automate = [automate_training, automate_test]

# Exploratory Data Analysis
training_eda = automate_training.eda()
test_eda = automate_test.eda()

# Data Cleaning and Transformation
training_eda_visual = automate_training.eda_visual(
                                    figsize_barchart = (55, 10),
                                    figsize_heatmap = (15, 10),
                                    figsize_histogram=(35, 20)
                                    )

for data in automate:
    data.reduce_data_memory_useage()
    data.drop_columns("Drop irrelevant columns")
    data.categorical_to_numerical() # If your data has categorical features

select_variables = automate_training.select_dependent_and_independent(predict = "Loan Status")

# Further Data Preparation and Segregation
unbalanced_dataset_check = automate_training.count_column_categories(column = "Specify what you are predicting")
split_data = automate_training.split_data()
fix_unbalanced_data = automate_training.fix_unbalanced_dataset(
                                            sampler = "RandomOverSampler",
                                            random_state = 0
                                            )

# Model Building
classifiers = [
        LogisticRegression(random_state = 0),
        RandomForestClassifier(random_state = 0),
        DecisionTreeClassifier(random_state = 0),
        XGBClassifier(random_state = 0)
        ]

build_model = automate_training.build_multiple_classifiers(
                                        classifiers = classifiers,
                                        kfold = 10,
                                        cross_validation = True,
                                        graph = True
                                        )