Machine Learning Program / Project - 01

Question 01: Predict the price of the Uber ride from a given pickup point to the agreed drop-off location. Perform following tasks:
1) Pre-process the dataset.
2) Identify outliers.
3) Check the correlation.
4) Implement linear regression and random forest regression models.
5) Evaluate the models and compare their respective scores like R2, RMSE, etc.

Download hole Program / Project code, by clicking following link:

go2collage/Machine Learning Program / Project - 01 (github.com)

Question ?

Answer

Programming Code:

Following code write in: ML_P01.py

# ML Project Program 01 

# import libraries
import numpy as np
import pandas as pd

# import dataset
data = pd.read_csv("uber_dataset/uber.csv")
# print first few data of uber dataset
data.head
# print information of Uber dataset
data.info()
# dtypes is nothing but the data types
# object is string data
# converting object to date & time
data["pickup_datetime"] = pd.to_datetime(data["pickup_datetime"])
data.info()
# successfully converted object to date & time by using to_datetime() method
# find missing values
data.isnull()
# find total number of missing values 
data.isnull().sum()
# 0 means false & 1 means True
# if Ture means null or missing values in dataset or in row
# drop the row if it has missing values

data.dropna(inplace = True)
# After drop missing value row

data.isnull().sum()
# Now create a Machine Learning Model

# import lib

from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error
# x is predictor variable
x = data.drop("fare_amount", axis = 1)

# y is target variable
y = data["fare_amount"]
# to apply model

x['pickup_datetime'] = pd.to_numeric(pd.to_datetime(x['pickup_datetime']))
x = x.loc[:, x.columns.str.contains('^Unnamed')]
x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2)

# testing data is 20%
# training data is 80%, allocated to model
# creating Linear Regression model

lrmodel = LinearRegression()
lrmodel.fit(x_train, y_train)
# model is created
# prediction

pred = lrmodel.predict(x_test)
# Calculating RMSE
lrmodelrmse = np.sqrt(mean_squared_error(pred, y_test))
print("RMSE error is: ",lrmodelrmse)
# Random Forest Regression

from sklearn.ensemble import RandomForestRegressor

# create RFR Model
rfrmodel = RandomForestRegressor(n_estimators = 100, random_state = 101)
# fit the forest

rfrmodel.fit(x_train, y_train)
rfrmodel_pred = rfrmodel.predict(x_test)
# Calculate RMSE for RFR

rfrmodel_rmse = np.sqrt(mean_squared_error(rfrmodel_pred, y_test))
print("RFR RMSE error is: ", rfrmodel_rmse)

# prediction

pred = lrmodel.predict(x_test)
print("hh",pred)
lrmodel.predict(x_test)
from sklearn import metrics

# R2 score

# R2 score Linear Regression
metrics.r2_score(y_test, pred)
# R2 score RF Model
metrics.r2_score(y_test, rfrmodel_pred)
# R2 score Linear Regression is 894% that means model not fit.
# R2 score RF Model is: 52%

# Random Forest Model best fit for this dataset, is perfect

# Thanks For Reading.

Output: