Just some Machine Learning stuff using sklearn

Training

model.fit(inputs,target) OR model.fit(inputs.values,target)

Prediction

model.predict(test_inputs) OR model.predict(test_inputs.values)

Accuracy

model.score(test_inputs,test_target)

Prediction Probability

model.predict_proba(test_inputs)

To get a list of all the parameters to a model

model.get_params(deep=True)

Scale the data

sklearn.preprocessing.MinMaxScaler

scaler = MinMaxScaler()
scaler.fit(df[['Age']])
df.Age = scaler.transform(df[['Age']])

To check if any of the columns have null values

dataset.columns[dataset.isna().any()]

To check the count of all columns having.not having null values

dataset.isna().sum()

To check the total number of NANs in a column

dataset['Column_name'].isna().sum()

Using pandas to read csv

import pandas as pd
dataset = pd.read_csv('data.csv')

Use datasets from sklearn

from sklearn.datasets import load_iris
dataset = load_iris()

Tuning the hyperparameters

Use GridSearch
sklearn.model_selection.GridSearchCV https://github.com/karenpinto1602/ML_Models/blob/main/Hyperparameter%20Tuning/Excercise/digitsHyperParameter.ipynb

Dealing with Overfitting using L1 and L2 Regularization

https://github.com/karenpinto1602/ML_Models/blob/main/Regularization