Price Prediction Of Diamonds

#python #datascience #machinelearning #deeplearning

I used Decision Tree, Random Forest, Linear, K-Neighbors and XGBoost Regressions with Python. I chose the best model with the Cross-Validation method. I used the R-Squared method to check that it gives a consistent result. As a result predicted with nearly 99% success rate. There is no 'Overfitting' problem.

I used the 10-Layer Cross-Validation method. The results are below:

It is XGBoost that works best in these datas. I used this regression model for prediction.

	# Dataset : https://raw.githubusercontent.com/tidyverse/ggplot2/master/data-raw/diamonds.csv
	# Alican AKCA - 20.01.2021

	#### Required Libraries

	# For Visualization and Calculation
	import matplotlib.pyplot as plt
	import matplotlib as mpl
	import matplotlib.pylab as pylab
	import seaborn as sns
	import numpy as np
	import re
	import pandas as pd

	# For Processing and Prediction Models

	from sklearn.preprocessing import OneHotEncoder, LabelEncoder
	from sklearn.model_selection import train_test_split
	from sklearn.preprocessing import StandardScaler
	from sklearn.decomposition import PCA

	from sklearn.tree import DecisionTreeRegressor
	from sklearn.ensemble import RandomForestRegressor
	from sklearn.linear_model import LinearRegression
	from xgboost import XGBRegressor
	from sklearn.neighbors import KNeighborsRegressor
	from sklearn.model_selection import cross_val_score
	from sklearn.metrics import mean_squared_error
	from sklearn import metrics
	from sklearn.model_selection import cross_val_score

	# Loading Dataset
	diamondsData=pd.read_csv('https://raw.githubusercontent.com/tidyverse/ggplot2/master/data-raw/diamonds.csv') # Or you can use csv file directory.

	# Encoding Labels, This dataset has a categorical data in columns

	encoding_diamondsData = diamondsData.copy()
	dataTypesOf_diamondsData = (diamondsData.dtypes =="object")
	objectColumns = list(dataTypesOf_diamondsData[dataTypesOf_diamondsData].index)

	# Applying label encoder to these columns.

	label_encoder = LabelEncoder()
	for enc in encoding_diamondsData:
	encoding_diamondsData[enc] = label_encoder.fit_transform(encoding_diamondsData[enc])

	# We are preparing data.
	X= encoding_diamondsData.drop(["price"],axis =1)
	y= encoding_diamondsData["price"]

	# We separated the prepared data for testing and training. We can change the separation rate.

	x_train, x_test, y_train, y_test = train_test_split(X, y,test_size=0.33, random_state=7)

	# Scaling process

	sc=StandardScaler()
	X_train = sc.fit_transform(x_train)
	X_test = sc.transform(x_test)

	# We gave our data to our 1st model

	lr = LinearRegression()
	lr.fit(X_train,y_train)
	lr_predictedData = lr.predict(X_test)

	# We gave our data to our 2nd model

	dtr = DecisionTreeRegressor()
	dtr.fit(X_train,y_train)
	dtr_predictedData = dtr.predict(X_test)

	# We gave our data to our 3th model

	rfr = RandomForestRegressor()
	rfr.fit(X_train,y_train)
	rfr_predictedData = rfr.predict(X_test)

	# We gave our data to our 4th model

	knn = KNeighborsRegressor()
	knn.fit(X_train,y_train)
	knn_predictedData = knn.predict(X_test)

	# We gave our data to our 5th model

	xgb = XGBRegressor()
	xgb.fit(X_train,y_train)
	xgb_predictedData = xgb.predict(X_test)

	# We applied cross-validation process.
	# We will list the results, look at the scores and find the best model.

	cv_scores = []
	cv_scores.append(cross_val_score(lr,X_train,y_train,scoring="neg_root_mean_squared_error", cv=10))
	cv_scores.append(cross_val_score(dtr,X_train,y_train,scoring="neg_root_mean_squared_error", cv=10))
	cv_scores.append(cross_val_score(rfr,X_train,y_train,scoring="neg_root_mean_squared_error", cv=10))
	cv_scores.append(cross_val_score(knn,X_train,y_train,scoring="neg_root_mean_squared_error", cv=10))
	cv_scores.append(cross_val_score(xgb,X_train,y_train,scoring="neg_root_mean_squared_error", cv=10))

	for i in cv_scores:
	print(i)

	# The result is usually not surprising,
	# Let's do the guesswork on the XGBoost model

	pred = xgb.predict(X_test)

	# Let's also use the R-Squared method for verification

	print("R^2:",metrics.r2_score(y_test, pred))
	print("Adjusted R^2:",1 - (1-metrics.r2_score(y_test, pred))*(len(y_test)-1)/(len(y_test)-X_test.shape[1]-1))

	# Visualization

	plt.figure(figsize=(15,10))
	plt.title('Model Evaluation')
	plt.scatter(range(0,len(X_test)),y_test,label='True',s=3, color = "blue")
	plt.scatter(range(0,len(X_test)),pred,label='Predicted',s=3, color = "orange")
	plt.xlabel('Index')
	plt.ylabel('Price')
	plt.legend()
	plt.show()