Name:Umesh Mali
Class:BE
Div:A
Rollno:44
Title: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.
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import r2_score, mean_squared_error
# Load the dataset
df = pd.read_csv("Uber.csv")
df.head()
Unnamed: 0 key fare_amount \
0 24238194 2015-05-07 19:52:06.0000003 7.5
1 27835199 2009-07-17 20:04:56.0000002 7.7
2 44984355 2009-08-24 21:45:00.00000061 12.9
3 25894730 2009-06-26 08:22:21.0000001 5.3
4 17610152 2014-08-28 17:47:00.000000188 16.0
pickup_datetime pickup_longitude pickup_latitude \
0 2015-05-07 19:52:06 UTC -73.999817 40.738354
1 2009-07-17 20:04:56 UTC -73.994355 40.728225
2 2009-08-24 21:45:00 UTC -74.005043 40.740770
3 2009-06-26 08:22:21 UTC -73.976124 40.790844
4 2014-08-28 17:47:00 UTC -73.925023 40.744085
dropoff_longitude dropoff_latitude passenger_count
0 -73.999512 40.723217 1
1 -73.994710 40.750325 1
2 -73.962565 40.772647 1
3 -73.965316 40.803349 3
4 -73.973082 40.761247 5
df.isnull().sum()
Unnamed: 0 0
key 0
fare_amount 0
pickup_datetime 0
pickup_longitude 0
pickup_latitude 0
dropoff_longitude 1
dropoff_latitude 1
passenger_count 0
dtype: int64
df=df.drop(['Unnamed: 0','key'],axis=1)
df['dropoff_longitude'].fillna(value=df['dropoff_longitude'].mean(),in
place=True)
df['dropoff_latitude'].fillna(value=df['dropoff_latitude'].mean(),inpl
ace=True)
df.isnull().sum()
fare_amount 0
pickup_datetime 0
pickup_longitude 0
pickup_latitude 0
dropoff_longitude 0
dropoff_latitude 0
passenger_count 0
dtype: int64
sns.boxplot(x=df["fare_amount"])
plt.show()
Q1 = df["fare_amount"].quantile(0.25)
Q3 = df["fare_amount"].quantile(0.75)
IQR = Q3 - Q1
threshold = 1.5
lower_bound = Q1 - threshold * IQR
upper_bound = Q3 + threshold * IQR
data_no_outliers = df[(df["fare_amount"] >= lower_bound) &
(df["fare_amount"] <= upper_bound)]
sns.boxplot(x=data_no_outliers["fare_amount"])
plt.show()
df.plot(kind="box",subplots=True, layout=(7, 2), figsize=(15, 20))
fare_amount Axes(0.125,0.786098;0.352273x0.0939024)
pickup_longitude Axes(0.547727,0.786098;0.352273x0.0939024)
pickup_latitude Axes(0.125,0.673415;0.352273x0.0939024)
dropoff_longitude Axes(0.547727,0.673415;0.352273x0.0939024)
dropoff_latitude Axes(0.125,0.560732;0.352273x0.0939024)
passenger_count Axes(0.547727,0.560732;0.352273x0.0939024)
dtype: object
X = df[['pickup_longitude', 'pickup_latitude', 'dropoff_longitude',
'dropoff_latitude', 'passenger_count']]
y = df['fare_amount']
X_train, X_test, y_train, y_test = train_test_split(X, y,
test_size=0.3)
linar_regression = LinearRegression()
linar_regression.fit(X_train, y_train)
LinearRegression()
y_pred=linar_regression.predict(X_test)
r2_lr = r2_score(y_test, y_pred)
r2_lr
9.512702113356752e-06
rmse_lr = mean_squared_error(y_test, y_pred)
rmse_lr
97.50890938668378
rmse_rf = np.sqrt(mean_squared_error(y_test, y_pred))
rmse_rf
9.87465996309158
