Machine Learning is the technology which is growing at a very fast pace in today's world. It is a subset of Artificial Intelligence. Some of the uses of machine learning in our daily life are Face recognition which we use to unlock our smartphones, Home assistants like Google Home, Amazon Alexa and Self-driving cars.

Machine Learning is teaching the computer to perform certain tasks without without being explicitly coded. It means that the system gets a certain degree of decision making capability. Machine Learning can be divided into three major categories:-

**Supervised Learning****Unsupervised Learning****Reinforcement Learning**

### Supervised Learning

Supervised Learning is known as supervised because in this method the model learns under the supervision of a teacher. The model has both input and output used for training. It means that the learner knows the output during the training process and trains the model to reduce the error in predict. The two major types of supervised learning methods are - Classification and Regression.

### Unsupervised Learning

Unsupervised Learning means that there is no supervisor for the process of learning. The model uses just input for training. The output is learned from the inputs only. The major types of unsupervised learning are Clustering in which we cluster similar types of things together and finding patterns in unlabelled datasets.

### Reinforcement Learning

Reinforcement Learning is the type of learning in which the model learns to take decisions based on rewards or punishment. The learner takes a decision and it receives feedback for the decision in the form of reward or punishment. The learner tries to maximize the rewards. It is used in solving Gaming algorithms or in Robotics where the robots learns by performing tasks and getting feedback in the form of rewards or punishment.

In this post I am going to explain the two major methods of **Supervised Learning** :-

**Classification**- In Classification, the output is discrete data. In simpler words, it means that we are going to categorize data based on certain features. Some of the basic examples are :- Differentiating between Apples and Oranges based on their shapes, color, texture, etc. In this example shape, color and texture are known as*features*and the output is "Apple" or "Orange" which are known as*Classes*. As the output is known as classes therefore the method is called*Classification*.**Regression**- In Regression, the output is continuous data. In this method, we predict the trends of data based on the features and the result does not belong to a certain category or class, it gives a numeric output which is real number. Some of the basic examples are:- Predicting the House Prices based on certain features like*size of the house*,*location of the house*, and*no. of floors*, etc. Another example of regression is predicting the sales of a certain good or the stock price of a certain company.

Python provides a lot of tools for performing Classification and Regression. One of the most used library is **scikit-learn**. It provides many models for Machine Learning.

The basic steps of supervised machine learning are-

- Loading the necessary libraries
- Loading the dataset
- Splitting the dataset into training and test set
- Training the model
- Evaluating the model

### Loading the Libraries

```
#Numpy deals with large arrays and linear algebra
import numpy as np
# Library for data manipulation and analysis
import pandas as pd
# Metrics for Evaluation of model Accuracy and F1-score
from sklearn.metrics import f1_score,accuracy_score
#Importing the Decision Tree from scikit-learn library
from sklearn.tree import DecisionTreeClassifier
# For splitting of data into train and test set
from sklearn.model_selection import train_test_split
```

### Loading the Dataset

```
train=pd.read_csv("/input/hcirs-ctf/train.csv")
# read_csv function of pandas reads the data in CSV format
# from path given and stores in the variable named train
# the data type of train is DataFrame
```

### Splitting into Train & Test set

```
#first we split our data into input and output
# y is the output and is stored in "Class" column of dataframe
# X contains the other columns and are features or input
y = train.Class
train.drop(['Class'], axis=1, inplace=True)
X = train
# Now we split the dataset in train and test part
# here the train set is 75% and test set is 25%
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=2)
```

### Training the model

```
# Training the model is as simple as this
# Use the function imported above and apply fit() on it
DT= DecisionTreeClassifier()
DT.fit(X_train,y_train)
```

### Evaluating the model

```
# We use the predict() on the model to predict the output
pred=DT.predict(X_test)
# for classification we use accuracy and F1 score
print(accuracy_score(y_test,pred))
print(f1_score(y_test,pred))
# for regression we use R2 score and MAE(mean absolute error)
# all other steps will be same as classification as shown above
from sklearn.metrics import mean_absolute_error
from sklearn.metrics import r2_score
print(mean_absolute_error(y_test,pred))
print(mean_absolute_error(y_test,pred))
```

As I have shown the basic steps and how to do the Classification and Regression now its time to learn about some Classification and Regression methods. I have compiled a collection of 10 Classification and 10 Regression functions which are popular. Import these methods and use in place of `DecisionTreeClassifier()`

and enjoy Machine Learning.

# 10 popular Classification Methods

### Logistic Regression

```
from sklearn.linear_model import LogisticRegression
```

### Support Vector Machine

```
from sklearn.svm import SVC
```

### Naive Bayes(Guassian, Multinomial)

```
from sklearn.naive_bayes import GaussianNB
from sklearn.naive_bayes import MultinomialNB
```

### Stochastic Gradient Descent Classifier

```
from sklearn.linear_model import SGDClassifier
```

### KNN (k-nearest neighbour)

```
from sklearn.neighbors import KNeighborsClassifier
```

### Decision Tree

```
from sklearn.tree import DecisionTreeClassifier
```

### Random Forest

```
from sklearn.ensemble import RandomForestClassifier
```

### Gradient Boosting Classifier

```
from sklearn.ensemble import GradientBoostingClassifier
```

### LGBM Classifier

```
from lightgbm import LGBMClassifier
```

### XGBoost Classifier

```
from xgboost.sklearn import XGBClassifier
```

# 10 popular Regression Methods

### Linear Regression

```
from sklearn.linear_model import LinearRegression
```

### LGBM Regressor

```
from lightgbm import LGBMRegressor
```

### XGBoost Regressor

```
from xgboost.sklearn import XGBRegressor
```

### CatBoost Regressor

```
from catboost import CatBoostRegressor
```

### Stochastic Gradient Descent Regressor

```
from sklearn.linear_model import SGDRegressor
```

### Kernel Ridge Regression

```
from sklearn.kernel_ridge import KernelRidge
```

### Elastic Net Regression

```
from sklearn.linear_model import ElasticNet
```

### Bayesian Ridge Regression

```
from sklearn.linear_model import BayesianRidge
```

### Gradient Boosting Regression

```
from sklearn.ensemble import GradientBoostingRegressor
```

### Support Vector Machine

```
from sklearn.svm import SVR
```

I hope it was helpful for the Machine Learning newbies and budding Data scientists. Please, upvote and share among your friends if you liked the post.

## Top comments (0)