House prices prediction

!pip install seaborn

import numpy as np import pandas as pd import matplotlib.pyplot as plt import seaborn as sns import mpl_toolkits %matplotlib inline

data = pd.read_csv("kc_house_data.csv")

data['bedrooms'].value_counts().plot(kind='bar') plt.title('number of Bedroom') plt.xlabel('Bedrooms') plt.ylabel('Count') sns.despine

plt.figure(figsize=(10,10)) sns.jointplot(x=data.lat.values, y=data.long.values, size=10) plt.ylabel('Longitude', fontsize=12) plt.xlabel('Latitude', fontsize=12) plt.show() # plt1 = plt() sns.despine

plt.scatter(data.price,data.sqft_living) plt.title("Price vs Square Feet")

plt.scatter(data.price,data.long) plt.title("Price vs Location of the area")

plt.scatter(data.price,data.lat) plt.xlabel("Price") plt.ylabel('Latitude') plt.title("Latitude vs Price")

plt.scatter(data.bedrooms,data.price) plt.title("Bedroom and Price ") plt.xlabel("Bedrooms") plt.ylabel("Price") plt.show() sns.despine

plt.scatter((data['sqft_living']+data['sqft_basement']),data['price'])

plt.scatter(data.waterfront,data.price) plt.title("Waterfront vs Price ( 0= no waterfront)")

train1 = data.drop(['id', 'price'],axis=1)

data.floors.value_counts().plot(kind='bar')

plt.scatter(data.floors,data.price)

plt.scatter(data.condition,data.price)

plt.scatter(data.zipcode,data.price) plt.title("Which is the pricey location by zipcode?")

from sklearn.linear_model import LinearRegression

reg = LinearRegression()

labels = data['price'] conv_dates = [1 if values == 2014 else 0 for values in data.date ] data['date'] = conv_dates train1 = data.drop(['id', 'price'],axis=1)

from sklearn.model_selection import train_test_split

x_train , x_test , y_train , y_test = train_test_split(train1 , labels , test_size = 0.10,random_state =2)

reg.fit(x_train,y_train)

reg.score(x_test,y_test)

from sklearn import ensemble clf = ensemble.GradientBoostingRegressor(n_estimators = 400, max_depth = 5, min_samples_split = 2, learning_rate = 0.1, loss = 'ls')

clf.fit(x_train, y_train)

clf.score(x_test,y_test)

t_sc = np.zeros((400),dtype=np.float64)

y_pred = reg.predict(x_test)

for i,y_pred in enumerate(clf.staged_predict(x_test)): t_sc[i]=clf.loss_(y_test,y_pred)

testsc = np.arange((400))+1

plt.figure(figsize=(12, 6)) plt.subplot(1, 2, 1) plt.plot(testsc,clf.train_score_,'b-',label= 'Set dev train') plt.plot(testsc,t_sc,'r-',label = 'set dev test')

from sklearn.preprocessing import scale from sklearn.decomposition import PCA

pca = PCA()

pca.fit_transform(scale(train1))