#ml 1 import pandas as pd import numpy as np import matplotlib.pyplot as plt import warnings import seaborn as sns #We do not want to see warnings warnings.filterwarnings("ignore")

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

df = data.copy()

df.head

df.info()

df["pickup_datetime"] = pd.to_datetime(df["pickup_datetime"])

df.info()

df.describe() df.isnull().sum() df.drop(['key', 'Unnamed: 0'],axis = 1,inplace=True) df2=df.drop(["pickup_datetime"],axis = 1) df2 df2.corr()

fig,axis = plt.subplots(figsize = (10,6)) sns.heatmap(df2.corr(),annot = True)

df.dropna(inplace=True) df.plot(kind = "box",subplots = True,layout = (7,2),figsize=(15,20)) #Boxplot shows that dataset is free from outliers

def remove_outlier(df1, col): Q1 = df1[col].quantile(0.25) Q3 = df1[col].quantile(0.75) IQR = Q3 - Q1 lower_whisker = Q1 - 1.5 * IQR upper_whisker = Q3 + 1.5 * IQR df[col] = np.clip(df1[col], lower_whisker, upper_whisker) return df1

def treat_outliers_all(df1, col_list): for c in col_list: df1 = remove_outlier(df, c) return df1

df = treat_outliers_all(df, df.iloc[:, 0::])

df.plot(kind="box", subplots=True, layout=(7, 2), figsize=(15, 20)) # Boxplot shows that dataset is free from outliers

#Check the missing values now df.isnull().sum()

from sklearn.model_selection import train_test_split from sklearn.preprocessing import StandardScaler

datatime_column = df['pickup_datetime'] df.drop('pickup_datetime', axis=1, inplace=True)

Scale the DataFrame without the datetime column

standard_scaler = StandardScaler() df_scaled_array = standard_scaler.fit_transform(df)

Convert the scaled array back to a DataFrame

df_scaled = pd.DataFrame(df_scaled_array, columns=df.columns)

Add the pickup_datetime column back

df_scaled['pickup_datetime'] = datatime_column.reset_index(drop=True)

#Take x as predictor variable x = df_scaled.drop("fare_amount", axis = 1) #And y as target variable y = df_scaled['fare_amount']

x['pickup_datetime'] = pd.to_numeric(pd.to_datetime(x['pickup_datetime']))

x_train, x_test, y_train, y_test = train_test_split(x, y, test_size = 0.2, random_state = 1)

from sklearn.linear_model import LinearRegression

lrmodel = LinearRegression() lrmodel.fit(x_train, y_train)

predict = lrmodel.predict(x_test)

from sklearn.metrics import mean_squared_error , r2_score lrmodelrmse = np.sqrt(mean_squared_error(y_test,predict)) r2 = r2_score(y_test,predict) print("RMSE error for the model is ", lrmodelrmse) print("R2 score for the model is ", r2)

#Let's Apply Random Forest Regressor from sklearn.ensemble import RandomForestRegressor rfrmodel = RandomForestRegressor(n_estimators = 100, random_state = 101)

#Fit the Forest rfrmodel.fit(x_train, y_train) rfrmodel_pred = rfrmodel.predict(x_test)

#Errors for the forest rfrmodel_rmse = np.sqrt(mean_squared_error(y_test , rfrmodel_pred)) rf_r2 = r2_score(y_test,rfrmodel_pred) print("RMSE value for Random Forest is:",rfrmodel_rmse) print("R2 score for the model is ", rf_r2)