saimadhu-polamuri

## dependencies
import pandas as pd
import nltk
import numpy as np
from nltk.corpus import stopwords
from nltk.tokenize import sent_tokenize as st
from nltk.stem import WordNetLemmatizer as wordnet
import re

saimadhu-polamuri

pip install nltk
pip install pandas
pip install numpy 

## After installing the pacakges run the below code.
nltk.download()

saimadhu-polamuri

## Ridge Regression Predictions
final_model = Ridge(alpha=0.25)
final_model.fit(X_train, y_train)

print(final_model.score(X_test, y_test))

## Output: 0.6973569341182368

saimadhu-polamuri

## Ridge regression

## two lists to hold alpha values and cross-validation scores
alpha = []
ridge_scores = []

## loop over different alpha values
for i in range(1,10):
    ridge_model = Ridge(alpha=0.25*i)

saimadhu-polamuri

# Lasso regression

## two lists to hold alpha values and cross-validation scores
alpha = []
lasso_scores = []

## we’ll be looping over different alpha values to find the one which gives us the best score.
## loop over different alpha values
for i in range(1,10):

saimadhu-polamuri

## Linear regression model
linearModel = LinearRegression() 
linearModel.fit(X_train, y_train) 
  
## Evaluating the model
print(linearModel.score(X_test, y_test)) 


"""

saimadhu-polamuri

## import the required libraries
import pandas as pd 
import numpy as np 
import matplotlib.pyplot as plt
from statistics import mean 
from sklearn.linear_model import LinearRegression, Ridge, Lasso 
from sklearn.model_selection import train_test_split, cross_val_score 

## load the dataset
df = pd.read_csv("kc_house_data.csv")

saimadhu-polamuri

## PCA Visualization
a_std = pca.transform(transformed)
plt.figure()
plt.title(label="PCA Visualization", fontsize=30, color="blue")
plt.xlabel("Principal Component 1")
plt.ylabel("Principal Component 2")
plt.title(label="PCA Visualization", fontsize=40, color="blue")
plt.scatter(a_std[:, 0], a_std[:, 1], c=b)

saimadhu-polamuri

std = StandardScaler()
transformed = StandardScaler().fit_transform(a)

## Two PCA components
pca = convers_pca(no_of_components=2)
pca.fit(transformed)
print(pca.eigen_vectors)
print(pca.eigen_values)
print(pca.sorted_components)

saimadhu-polamuri

class convers_pca():
    def __init__(self, no_of_components):
       self.no_of_components = no_of_components
       self.eigen_values = None
       self.eigen_vectors = None

    def transform(self, a):
       return np.dot(a - self.mean, self.projection_matrix.T)