Bike Sharing Demand Prediction
Objective: -
Bike sharing systems are a means of renting bicycles where the process of obtaining membership, rental, and bike return is automated via a network of kiosk locations throughout a city. Using these systems, people are able to rent a bike from a one location and return it to a different place on an as-needed basis. Currently, there are over 500 bike-sharing programs around the world.
The data generated by these systems makes them attractive for researchers because the duration of travel, departure location, arrival location, and time elapsed is explicitly recorded. Bike sharing systems therefore function as a sensor network, which can be used for studying mobility in a city. In this competition, participants are asked to combine historical usage patterns with weather data in order to forecast bike rental demand in the Capital Bikeshare program in Washington, D.C.
The goal of this challenge is to use this data to train a machine learning model to predict the bike sharing demand.
Step 1: Import all the required libraries
- Pandas : In computer programming, pandas is a software library written for the Python programming language for data manipulation and analysis and storing in a proper way. In particular, it offers data structures and operations for manipulating numerical tables and time series
- Sklearn : Scikit-learn (formerly scikits.learn) is a free software machine learning library for the Python programming language. It features various classification, regression and clustering algorithms including support vector machines, random forests, gradient boosting, k-means and DBSCAN, and is designed to interoperate with the Python numerical and scientific libraries NumPy and SciPy. The library is built upon the SciPy (Scientific Python) that must be installed before you can use scikit-learn.
- Pickle : Python pickle module is used for serializing and de-serializing a Python object structure. Pickling is a way to convert a python object (list, dict, etc.) into a character stream. The idea is that this character stream contains all the information necessary to reconstruct the object in another python script.
- Seaborn : Seaborn is a Python data visualization library based on matplotlib. It provides a high-level interface for drawing attractive and informative statistical graphics.
- Matplotlib : Matplotlib is a plotting library for the Python programming language and its numerical mathematics extension NumPy. It provides an object-oriented API for embedding plots into applications using general-purpose GUI toolkits like Tkinter, wxPython, Qt, or GTK.
#Loading libraries
import pandas as pd
import seaborn as sns
from sklearn.model_selection import train_test_split
from sklearn import preprocessing
import sklearn.linear_model
import sklearn
import pickle
import numpy as np
import matplotlib.pyplot as plt
from sklearn.preprocessing import OneHotEncoder
from matplotlib.pyplot import figure
import matplotlib.pyplot as plt
from sklearn.metrics import mean_absolute_percentage_error
from sklearn.metrics import mean_squared_error
from sklearn.metrics import r2_score
from sklearn.preprocessing import scale
from sklearn.linear_model import LinearRegression, Ridge, RidgeCV, Lasso, LassoCV
from sklearn.model_selection import KFold, cross_val_score, train_test_split
from sklearn.metrics import mean_squared_error
from sklearn.decomposition import PCA
import warnings
warnings.filterwarnings('ignore')Step 2 : Read dataset and basic details of dataset
Goal:- In this step we are going to read the dataset, view the dataset and analysis the basic details like total number of rows and columns, what are the column data types and see to need to create new column or not.
In this stage we are going to read our problem dataset and have a look on it.
#loading the dataset
try:
df = pd.read_csv('C:/Users/YAJENDRA/Documents/final notebooks/Bike Sharing Demand Prediction/Data/data.csv') #Path for the file
print('Data read done successfully...')
except (FileNotFoundError, IOError):
print("Wrong file or file path")Data read done successfully...# To view the content inside the dataset we can use the head() method that returns a specified number of rows, string from the top.
# The head() method returns the first 5 rows if a number is not specified.
df.head()
Dataset: -
Kaggle is hosting this competition for the machine learning community to use for fun and practice. This dataset was provided by Hadi Fanaee Tork using data from Capital Bikeshare. We also thank the UCI machine learning repository for hosting the dataset. If you use the problem in publication, please cite:
Fanaee-T, Hadi, and Gama, Joao, Event labeling combining ensemble detectors and background knowledge, Progress in Artificial Intelligence (2013): pp. 1–15, Springer Berlin Heidelberg.
Attribute Information:
- count — number of total rentals
Other features:
- DateTime (Date and time of the record)
- season — 1 = spring, 2 = summer, 3 = fall, 4 = winter
- holiday — whether the day is considered a holiday
- workingday — whether the day is neither a weekend nor holiday
- weather — Clear, Few clouds, Partly cloudy, Partly cloudy, Mist + Cloudy, Mist + Broken clouds, Mist + Few clouds, Mist, Light Snow, Light Rain + Thunderstorm + Scattered clouds, Light Rain + Scattered clouds, Heavy Rain + Ice Pallets + Thunderstorm + Mist, Snow + Fog
- temp — temperature in Celsius
- atemp — “feels like” temperature in Celsius
- humidity — relative humidity
- windspeed — wind speed
- casual — number of non-registered user rentals initiated
- registered — number of registered user rentals initiated
Step3: Data Preprocessing
Why need of Data Preprocessing?
Preprocessing data is an important step for data analysis. The following are some benefits of preprocessing data:
- It improves accuracy and reliability. Preprocessing data removes missing or inconsistent data values resulting from human or computer error, which can improve the accuracy and quality of a dataset, making it more reliable.
- It makes data consistent. When collecting data, it’s possible to have data duplicates, and discarding them during preprocessing can ensure the data values for analysis are consistent, which helps produce accurate results.
- It increases the data’s algorithm readability. Preprocessing enhances the data’s quality and makes it easier for machine learning algorithms to read, use, and interpret it.
After we read the data, we can look at the data using:
# count the total number of rows and columns.
print ('The train data has {0} rows and {1} columns'.format(df.shape[0],df.shape[1]))The train data has 10886 rows and 12 columns
By analysing the problem statement and the dataset, we get to know that the target variable is “count” column which is continuous ans shows the count of rental bikes.
df['count'].value_counts()5 169
4 149
3 144
6 135
2 132
...
801 1
629 1
825 1
589 1
636 1
Name: count, Length: 822, dtype: int64
The df.value_counts() method counts the number of types of values a particular column contains.
df.shape(10886, 12)
The df.shape method shows the shape of the dataset.
We can identify that their are 10886 rows and 12 columns.
df.info()<class 'pandas.core.frame.DataFrame'>
RangeIndex: 10886 entries, 0 to 10885
Data columns (total 12 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 datetime 10886 non-null object
1 season 10886 non-null int64
2 holiday 10886 non-null int64
3 workingday 10886 non-null int64
4 weather 10886 non-null int64
5 temp 10886 non-null float64
6 atemp 10886 non-null float64
7 humidity 10886 non-null int64
8 windspeed 10886 non-null float64
9 casual 10886 non-null int64
10 registered 10886 non-null int64
11 count 10886 non-null int64
dtypes: float64(3), int64(8), object(1)
memory usage: 1020.7+ KB
The df.info() method prints information about a DataFrame including the index dtype and columns, non-null values and memory usage.
df.iloc[1]datetime 2011-01-01 01:00:00
season 1
holiday 0
workingday 0
weather 1
temp 9.02
atemp 13.635
humidity 80
windspeed 0.0
casual 8
registered 32
count 40
Name: 1, dtype: object
df.iloc[ ] is primarily integer position based (from 0 to length-1 of the axis), but may also be used with a boolean array. The iloc property gets, or sets, the value(s) of the specified indexes.
Data Type Check for every column
Why data type check is required?
Data type check helps us with understanding what type of variables our dataset contains. It helps us with identifying whether to keep that variable or not. If the dataset contains contiguous data, then only float and integer type variables will be beneficial and if we have to classify any value then categorical variables will be beneficial.
objects_cols = ['object']
objects_lst = list(df.select_dtypes(include=objects_cols).columns)print("Total number of categorical columns are ", len(objects_lst))
print("There names are as follows: ", objects_lst)Total number of categorical columns are 1
There names are as follows: ['datetime']int64_cols = ['int64']
int64_lst = list(df.select_dtypes(include=int64_cols).columns)print("Total number of numerical columns are ", len(int64_lst))
print("There names are as follows: ", int64_lst)Total number of numerical columns are 8
There names are as follows: ['season', 'holiday', 'workingday', 'weather', 'humidity', 'casual', 'registered', 'count']float64_cols = ['float64']
float64_lst = list(df.select_dtypes(include=float64_cols).columns)print("Total number of numerical columns are ", len(float64_lst))
print("There names are as follows: ", float64_lst)Total number of numerical columns are 3
There names are as follows: ['temp', 'atemp', 'windspeed']
In the dataset , the column named as “datetime”, we will count the unique values of datetime column which is shown below and also the length of dataset.
len(df['datetime'].unique()), df.shape[0](10886, 10886)
- The uniqueness of “datetime” column and length of the dataset both are same that means there is no sense to keep datetime column so we have to drop that column:
df = df.drop(['datetime'],axis=1)#count the total number of rows and columns.
print ('The new dataset has {0} rows and {1} columns'.format(df.shape[0],df.shape[1]))The new dataset has 10886 rows and 11 columns
Step 2 Insights: -
- We have total 11 features where 8 of them are integer type while others are float type.
- Drop “datetime” columns.
After this step we have to calculate various evaluation parameters which will help us in cleaning and analysing the data more accurately.
Step 3: Descriptive Analysis
Goal/Purpose: Finding the data distribution of the features. Visualization helps to understand data and also to explain the data to another person.
Things we are going to do in this step:
- Mean
- Median
- Mode
- Standard Deviation
- Variance
- Null Values
- NaN Values
- Min value
- Max value
- Count Value
- Quatilers
- Correlation
- Skewness
df.describe()
The df.describe() method returns description of the data in the DataFrame. If the DataFrame contains numerical data, the description contains these information for each column: count — The number of not-empty values. mean — The average (mean) value.
Measure the variability of data of the dataset
Variability describes how far apart data points lie from each other and from the center of a distribution.
1. Standard Deviation
The standard deviation is the average amount of variability in your dataset.
It tells you, on average, how far each data point lies from the mean. The larger the standard deviation, the more variable the data set is and if zero variance then there is no variability in the dataset that means there no use of that dataset.
So, it helps in understanding the measurements when the data is distributed. The more the data is distributed, the greater will be the standard deviation of that data.Here, you as an individual can determine which company is beneficial in long term. But, if you didn’t know the SD you would have choosen a wrong compnay for you.
df.std()season 1.116174
holiday 0.166599
workingday 0.466159
weather 0.633839
temp 7.791590
atemp 8.474601
humidity 19.245033
windspeed 8.164537
casual 49.960477
registered 151.039033
count 181.144454
dtype: float64
We can also understand the standard deviation using the below function.
def std_cal(df,float64_lst):
cols = ['normal_value', 'zero_value']
zero_value = 0
normal_value = 0
for value in float64_lst:
rs = round(df[value].std(),6)
if rs > 0:
normal_value = normal_value + 1
elif rs == 0:
zero_value = zero_value + 1
std_total_df = pd.DataFrame([[normal_value, zero_value]], columns=cols)
return std_total_dfint64_cols = ['int64']
int64_lst = list(df.select_dtypes(include=int64_cols).columns)
std_cal(df,int64_lst)
float64_cols = ['float64']
float64_lst = list(df.select_dtypes(include=float64_cols).columns)
std_cal(df,float64_lst)
zero_value -> is the zero variance and when then there is no variability in the dataset that means there no use of that dataset.
2. Variance
The variance is the average of squared deviations from the mean. A deviation from the mean is how far a score lies from the mean.
Variance is the square of the standard deviation. This means that the units of variance are much larger than those of a typical value of a data set.
Why do we used Variance ?
By Squairng the number we get non-negative computation i.e. Disperson cannot be negative. The presence of variance is very important in your dataset because this will allow the model to learn about the different patterns hidden in the data
df.var()season 1.245845
holiday 0.027755
workingday 0.217304
weather 0.401751
temp 60.708872
atemp 71.818856
humidity 370.371306
windspeed 66.659670
casual 2496.049219
registered 22812.789514
count 32813.313153
dtype: float64
We can also understand the Variance using the below function.
zero_cols = []
def var_cal(df,float64_lst):
cols = ['normal_value', 'zero_value']
zero_value = 0
normal_value = 0
for value in float64_lst:
rs = round(df[value].var(),6)
if rs > 0:
normal_value = normal_value + 1
elif rs == 0:
zero_value = zero_value + 1
zero_cols.append(value)
var_total_df = pd.DataFrame([[normal_value, zero_value]], columns=cols)
return var_total_dfvar_cal(df, int64_lst)
var_cal(df, float64_lst)zero_value -> Zero variance means that there is no difference in the data values, which means that they are all the same.
Measure central tendency
A measure of central tendency is a single value that attempts to describe a set of data by identifying the central position within that set of data. As such, measures of central tendency are sometimes called measures of central location. They are also classed as summary statistics.
Mean — The average value. Median — The mid point value. Mode — The most common value.
1. Mean
The mean is the arithmetic average, and it is probably the measure of central tendency that you are most familiar.
Why do we calculate mean?
The mean is used to summarize a data set. It is a measure of the center of a data set.
df.mean()season 2.506614
holiday 0.028569
workingday 0.680875
weather 1.418427
temp 20.230860
atemp 23.655084
humidity 61.886460
windspeed 12.799395
casual 36.021955
registered 155.552177
count 191.574132
dtype: float64
The average amount of bikes on rent are 191.
We can also understand the mean using the below function.
def mean_cal(df,int64_lst):
cols = ['normal_value', 'zero_value']
zero_value = 0
normal_value = 0
for value in int64_lst:
rs = round(df[value].mean(),6)
if rs > 0:
normal_value = normal_value + 1
elif rs == 0:
zero_value = zero_value + 1
mean_total_df = pd.DataFrame([[normal_value, zero_value]], columns=cols)
return mean_total_dfmean_cal(df, int64_lst)
mean_cal(df, float64_lst)zero_value -> that the mean of a paticular column is zero, which isn’t usefull in anyway and need to be drop.
2.Median
The median is the middle value. It is the value that splits the dataset in half.The median of a dataset is the value that, assuming the dataset is ordered from smallest to largest, falls in the middle. If there are an even number of values in a dataset, the middle two values are the median.
Why do we calculate median ?
By comparing the median to the mean, you can get an idea of the distribution of a dataset. When the mean and the median are the same, the dataset is more or less evenly distributed from the lowest to highest values.
df.median()season 3.000
holiday 0.000
workingday 1.000
weather 1.000
temp 20.500
atemp 24.240
humidity 62.000
windspeed 12.998
casual 17.000
registered 118.000
count 145.000
dtype: float64
We can also understand the median using the below function.
def median_cal(df,int64_lst):
cols = ['normal_value', 'zero_value']
zero_value = 0
normal_value = 0
for value in int64_lst:
rs = round(df[value].mean(),6)
if rs > 0:
normal_value = normal_value + 1
elif rs == 0:
zero_value = zero_value + 1
median_total_df = pd.DataFrame([[normal_value, zero_value]], columns=cols)
return median_total_dfmedian_cal(df, int64_lst)
median_cal(df, float64_lst)zero_value -> that the median of a paticular column is zero which isn’t usefull in anyway and need to be drop.
3. Mode
The mode is the value that occurs the most frequently in your data set. On a bar chart, the mode is the highest bar. If the data have multiple values that are tied for occurring the most frequently, you have a multimodal distribution. If no value repeats, the data do not have a mode.
Why do we calculate mode ?
The mode can be used to summarize categorical variables, while the mean and median can be calculated only for numeric variables. This is the main advantage of the mode as a measure of central tendency. It’s also useful for discrete variables and for continuous variables when they are expressed as intervals.
df.mode()
def mode_cal(df,int64_lst):
cols = ['normal_value', 'zero_value', 'string_value']
zero_value = 0
normal_value = 0
string_value = 0
for value in int64_lst:
rs = df[value].mode()[0]
if isinstance(rs, str):
string_value = string_value + 1
else:
if rs > 0:
normal_value = normal_value + 1
elif rs == 0:
zero_value = zero_value + 1
mode_total_df = pd.DataFrame([[normal_value, zero_value, string_value]], columns=cols)
return mode_total_dfmode_cal(df, list(df.columns))
zero_value -> that the mode of a paticular column is zero which isn’t usefull in anyway and need to be drop.
Null and Nan values
- Null Values
A null value in a relational database is used when the value in a column is unknown or missing. A null is neither an empty string (for character or datetime data types) nor a zero value (for numeric data types).
df.isnull().sum()season 0
holiday 0
workingday 0
weather 0
temp 0
atemp 0
humidity 0
windspeed 0
casual 0
registered 0
count 0
dtype: int64
As we notice that there is no null values in our dataset.
- Nan Values
NaN, standing for Not a Number, is a member of a numeric data type that can be interpreted as a value that is undefined or unrepresentable, especially in floating-point arithmetic.
df.isna().sum()season 0
holiday 0
workingday 0
weather 0
temp 0
atemp 0
humidity 0
windspeed 0
casual 0
registered 0
count 0
dtype: int64
As we notice that there is no nan values in our dataset.
No null or nan values shows that all the features are analysed to predict the demand of rental bikes.
Another way to remove null and nan values is to use the method “df.dropna(inplace=True)”.
Count of unique occurences of every value in all categorical value
objects_cols = ['object']
objects_lst = list(df.select_dtypes(include=objects_cols).columns)
for value in objects_lst:
print(f"{value:{10}} {df[value].value_counts()}")- Categorical data are variables that contain label values rather than numeric values.The number of possible values is often limited to a fixed set.
- Use Label Encoder to label the categorical data. Label Encoder is the part of SciKit Learn library in Python and used to convert categorical data, or text data, into numbers, which our predictive models can better understand.
Label Encoding refers to converting the labels into a numeric form so as to convert them into the machine-readable form. Machine learning algorithms can then decide in a better way how those labels must be operated. It is an important pre-processing step for the structured dataset in supervised learning.
Skewness
Skewness is a measure of the asymmetry of a distribution. A distribution is asymmetrical when its left and right side are not mirror images. A distribution can have right (or positive), left (or negative), or zero skewness
Why do we calculate Skewness ?
Skewness gives the direction of the outliers if it is right-skewed, most of the outliers are present on the right side of the distribution while if it is left-skewed, most of the outliers will present on the left side of the distribution
Below is the function to calculate skewness.
def right_nor_left(df, int64_lst):
temp_skewness = ['column', 'skewness_value', 'skewness (+ve or -ve)']
temp_skewness_values = []
temp_total = ["positive (+ve) skewed", "normal distrbution" , "negative (-ve) skewed"]
positive = 0
negative = 0
normal = 0
for value in int64_lst:
rs = round(df[value].skew(),4)
if rs > 0:
temp_skewness_values.append([value,rs , "positive (+ve) skewed"])
positive = positive + 1
elif rs == 0:
temp_skewness_values.append([value,rs,"normal distrbution"])
normal = normal + 1
elif rs < 0:
temp_skewness_values.append([value,rs, "negative (-ve) skewed"])
negative = negative + 1
skewness_df = pd.DataFrame(temp_skewness_values, columns=temp_skewness)
skewness_total_df = pd.DataFrame([[positive, normal, negative]], columns=temp_total)
return skewness_df, skewness_total_dffloat64_cols = ['float64']
float64_lst_col = list(df.select_dtypes(include=float64_cols).columns)
skew_df,skew_total_df = right_nor_left(df, float64_lst_col)skew_df
skew_total_df
int64_cols = ['int64']
int64_lst_col = list(df.select_dtypes(include=int64_cols).columns)
skew_df,skew_total_df = right_nor_left(df, int64_lst_col)skew_df
skew_total_df
We notice with the above results that we have following details:
- 7 columns are positive skewed
- 4 columns are negative skewed
Step 3 Insights: -
With the statistical analysis we have found that the data have a lot of skewness in them all the columns are positively skewed with mostly zero variance.
Statistical analysis is little difficult to understand at one glance so to make it more understandable we will perform visulatization on the data which will help us to understand the process easily.
Why we are calculating all these metrics?
Mean / Median /Mode/ Variance /Standard Deviation are all very basic but very important concept of statistics used in data science. Almost all the machine learning algorithm uses these concepts in data preprocessing steps. These concepts are part of descriptive statistics where we basically used to describe and understand the data for features in Machine learning
Benefits of Bike Sharing: -
Bike sharing programs have sprouted up in many cities and communities across the United States. Some in the cycling community believe these programs are beneficial and encourage riding by individuals who might not otherwise use a bicycle.
- Environmental benefit: Biking is great for the environment because it allows people to travel without releasing toxins and burning fuel. People who utilize bike sharing programs might use them instead of their own car or an Uber or taxi to travel around their area.
- Fitness: Biking is known to be an excellent exercise for both the body and the mind. Studies have shown that cycling offers tremendous cardiovascular benefits, helps stave off the effects of depression and anxiety, and increases overall fitness levels.
- Convenient to use: Public bicycle systems offer a convenient, easy-to-use system for citizens. There is no need to worry about maintenance and upkeep or bicycle storage. Also, cyclists don’t have to commit to making a bicycle purchase.
- A Greater embrace of cycling: As a community has more cyclists traveling the roads, they make changes to benefit the growing population. Bike lanes are built, and laws are written to protect cyclists. Having more cyclists on the road also means that drivers become more familiar with sharing the road with cyclists.
Step 4: Data Exploration
Goal/Purpose:
Graphs we are going to develop in this step
- Histogram of all columns to check the distrubution of the columns
- Distplot or distribution plot of all columns to check the variation in the data distribution
- Heatmap to calculate correlation within feature variables
- Boxplot to find out outlier in the feature columns
- Scatter Plot to show the relation between variables
1. Histogram
A histogram is a bar graph-like representation of data that buckets a range of classes into columns along the horizontal x-axis.The vertical y-axis represents the number count or percentage of occurrences in the data for each column
# Distribution in attributes
%matplotlib inline
import matplotlib.pyplot as plt
df.hist(bins=50, figsize=(30,30))
plt.show()
Histogram Insight: -
Histogram helps in identifying the following:
- View the shape of your data set’s distribution to look for outliers or other significant data points.
- Determine whether something significant has boccurred from one time period to another.
From the above histogram we observe that the bikes demand grows higher by number of users registered. It shows the customers who are registered more engaged and uses bike on rent.
Why Histogram?
It is used to illustrate the major features of the distribution of the data in a convenient form. It is also useful when dealing with large data sets (greater than 100 observations). It can help detect any unusual observations (outliers) or any gaps in the data.
From the above graphical representation we can identify that the highest bar represents the outliers which is above the maximum range.
We can also identify that the values are moving on the right side, which determines positive and the centered values determines normal skewness.
2. Distplot
A Distplot or distribution plot, depicts the variation in the data distribution. Seaborn Distplot represents the overall distribution of continuous data variables. The Seaborn module along with the Matplotlib module is used to depict the distplot with different variations in it
num = [f for f in df.columns if df.dtypes[f] != 'object']
nd = pd.melt(df, value_vars = num)
n1 = sns.FacetGrid (nd, col='variable', col_wrap=4, sharex=False, sharey = False)
n1 = n1.map(sns.distplot, 'value')
n1<seaborn.axisgrid.FacetGrid at 0x2098a547350>
Distplot Insights: -
Above is the distrution bar graphs to confirm about the statistics of the data about the skewness, the above results are:
- 7 columns are positive skewed and 4 columns are negative skewed.
- 1 column is added here i.e count which is our target variable ~ which is also +ve skewed. In that case we’ll need to cube root transform this variable so that it becomes normally distributed. A normally distributed (or close to normal) target variable helps in better modeling the relationship between target and independent variables
Why Distplot?
Skewness is demonstrated on a bell curve when data points are not distributed symmetrically to the left and right sides of the median on a bell curve. If the bell curve is shifted to the left or the right, it is said to be skewed.
We can observe that the bell curve is shifted to left we indicates positive skewness.As all the column are positively skewed we don’t need to do scaling.
Let’s proceed and check the distribution of the target variable.
#+ve skewed
df['count'].skew()1.2420662117180776
The target variable is positively skewed.A normally distributed (or close to normal) target variable helps in better modeling the relationship between target and independent variables.
3. Heatmap
A heatmap (or heat map) is a graphical representation of data where values are depicted by color.Heatmaps make it easy to visualize complex data and understand it at a glance
Correlation — A positive correlation is a relationship between two variables in which both variables move in the same direction. Therefore, when one variable increases as the other variable increases, or one variable decreases while the other decreases.
Correlation can have a value:
- 1 is a perfect positive correlation
- 0 is no correlation (the values don’t seem linked at all)
- -1 is a perfect negative correlation
#correlation plot
sns.set(rc = {'figure.figsize':(12,12)})
corr = df.corr().abs()
sns.heatmap(corr,annot=True)
plt.show()
Notice the last column from right side of this map. We can see the correlation of all variables against count . As you can see, some variables seem to be strongly correlated with the target variable. Here, a numeric correlation score will help us understand the graph better.
print (corr['count'].sort_values(ascending=False)[:15], '\n') #top 15 values
print ('----------------------------------------')
print (corr['count'].sort_values(ascending=False)[-5:]) #last 5 values`count 1.000000
registered 0.970948
casual 0.690414
temp 0.394454
atemp 0.389784
humidity 0.317371
season 0.163439
weather 0.128655
windspeed 0.101369
workingday 0.011594
holiday 0.005393
Name: count, dtype: float64
----------------------------------------
season 0.163439
weather 0.128655
windspeed 0.101369
workingday 0.011594
holiday 0.005393
Name: count, dtype: float64
Here we see that the registered feature is 97% correlated with the target variable.
This shows that registered users demand more bikes.
corr
Heatmap insights: -
As we know, it is recommended to avoid correlated features in your dataset. Indeed, a group of highly correlated features will not bring additional information (or just very few), but will increase the complexity of the algorithm, hence increasing the risk of errors.
Why Heatmap?
Heatmaps are used to show relationships between two variables, one plotted on each axis. By observing how cell colors change across each axis, you can observe if there are any patterns in value for one or both variables.
We will drop some columns which have correlation close to zero.
4. Boxplot
A boxplot is a standardized way of displaying the distribution of data based on a five number summary (“minimum”, first quartile [Q1], median, third quartile [Q3] and “maximum”).
Basically, to find the outlier in a dataset/column.
features = df.columns.tolist()
features.remove('count')sns.boxplot(data=df)<Axes: >
The dark points are known as Outliers. Outliers are those data points that are significantly different from the rest of the dataset. They are often abnormal observations that skew the data distribution, and arise due to inconsistent data entry, or erroneous observations.
Boxplot Insights: -
- Sometimes outliers may be an error in the data and should be removed. In this case these points are correct readings yet they are different from the other points that they appear to be incorrect.
- The best way to decide wether to remove them or not is to train models with and without these data points and compare their validation accuracy.
- So we will keep it unchanged as it won’t affect our model.
Here, we can see that most of the variables possess outlier values. It would take us days if we start treating these outlier values one by one. Hence, for now we’ll leave them as is and let our algorithm deal with them. As we know, tree-based algorithms are usually robust to outliers.
Why Boxplot?
Box plots are used to show distributions of numeric data values, especially when you want to compare them between multiple groups. They are built to provide high-level information at a glance, offering general information about a group of data’s symmetry, skew, variance, and outliers.
In the next step we will divide our cleaned data into training data and testing data.
Step 2: Data Preparation
Goal:-
Tasks we are going to in this step:
- Now we will spearate the target variable and feature columns in two different dataframe and will check the shape of the dataset for validation purpose.
- Split dataset into train and test dataset.
- Scaling on train dataset.
1. Now we spearate the target variable and feature columns in two different dataframe and will check the shape of the dataset for validation purpose.
# Separate target and feature column in X and y variable
target = 'count'
# X will be the features
X = df.drop(target,axis=1)
#y will be the target variable
y = df[target]y have target variable and X have all other variable.
Here in Bike Sharing Demand Prediction, count is the target variable.
X.info()<class 'pandas.core.frame.DataFrame'>
RangeIndex: 10886 entries, 0 to 10885
Data columns (total 10 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 season 10886 non-null int64
1 holiday 10886 non-null int64
2 workingday 10886 non-null int64
3 weather 10886 non-null int64
4 temp 10886 non-null float64
5 atemp 10886 non-null float64
6 humidity 10886 non-null int64
7 windspeed 10886 non-null float64
8 casual 10886 non-null int64
9 registered 10886 non-null int64
dtypes: float64(3), int64(7)
memory usage: 850.6 KBy0 16
1 40
2 32
3 13
4 1
...
10881 336
10882 241
10883 168
10884 129
10885 88
Name: count, Length: 10886, dtype: int64# Check the shape of X and y variable
X.shape, y.shape((10886, 10), (10886,))# Reshape the y variable
y = y.values.reshape(-1,1)# Again check the shape of X and y variable
X.shape, y.shape((10886, 10), (10886, 1))
2. Spliting the dataset in training and testing data.
Here we are spliting our dataset into 80/20 percentage where 80% dataset goes into the training part and 20% goes into testing part.
# Split the X and y into X_train, X_test, y_train, y_test variables with 80-20% split.
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)# Check shape of the splitted variables
X_train.shape, X_test.shape, y_train.shape, y_test.shape((8708, 10), (2178, 10), (8708, 1), (2178, 1))
Insights: -
Train test split technique is used to estimate the performance of machine learning algorithms which are used to make predictions on data not used to train the model.It is a fast and easy procedure to perform, the results of which allow you to compare the performance of machine learning algorithms for your predictive modeling problem. Although simple to use and interpret, there are times when the procedure should not be used, such as when you have a small dataset and situations where additional configuration is required, such as when it is used for classification and the dataset is not balanced.
In the next step we will train our model on the basis of our training and testing data.
Step 3: Model Training
Goal:
In this step we are going to train our dataset on different regression algorithms. As we know that our target variable is not discrete format so we have to apply regression algorithm. In our dataset we have the outcome variable or Dependent variable i.e Y having non discrete values. So we will use Regression algorithm.
Algorithms we are going to use in this step
- Linear Regression
- Lasso Regression
- Ridge Regression
- RandomForestRegressor
K-fold cross validation is a procedure used to estimate the skill of the model on new data. There are common tactics that you can use to select the value of k for your dataset. There are commonly used variations on cross-validation, such as stratified and repeated, that are available in scikit-learn
# Define kfold with 10 split
cv = KFold(n_splits=10, shuffle=True, random_state=42)The goal of cross-validation is to test the model’s ability to predict new data that was not used in estimating it, in order to flag problems like overfitting or selection bias and to give an insight on how the model will generalize to an independent dataset (i.e., an unknown dataset, for instance from a real problem).
1. Linear Regression
Linear regression is one of the easiest and most popular Machine Learning algorithms. It is a statistical method that is used for predictive analysis. Linear regression makes predictions for continuous/real or numeric variables.
Train set cross-validation
#Using Linear Regression Algorithm to the Training Set
from sklearn.linear_model import LinearRegression
li_R = LinearRegression() #Object Creation
li_R.fit(X_train, y_train)LinearRegression()
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LinearRegression
LinearRegression()#Accuracy check of trainig data
#Get R2 score
li_R.score(X_train, y_train)1.0#Accuracy of test data
li_R.score(X_test, y_test)1.0# Getting kfold values
li_scores = -1 * cross_val_score(li_R,
X_train,
y_train,
cv=cv,
scoring='neg_root_mean_squared_error')
li_scoresarray([1.48237144e-12, 1.44045499e-13, 1.44915313e-13, 1.01419418e-12,
1.10847777e-12, 1.36205068e-13, 1.95833617e-13, 2.27504259e-13,
2.28409085e-13, 4.03046487e-13])# Mean of the train kfold scores
li_score_train = np.mean(li_scores)
li_score_train5.085002722986164e-13
Prediction
Now we will perform prediction on the dataset using Linear Regression.
# Predict the values on X_test_scaled dataset
y_predicted = li_R.predict(X_test)Various parameters are calculated for analysing the predictions.
- Confusion Matrix 2)Classification Report 3)Accuracy Score 4)Precision Score 5)Recall Score 6)F1 Score
Confusion Matrix
A confusion matrix presents a table layout of the different outcomes of the prediction and results of a classification problem and helps visualize its outcomes. It plots a table of all the predicted and actual values of a classifier.
This diagram helps in understanding the concept of confusion matrix.
Evaluating all kinds of evaluating parameters.
Classification Report :
A classification report is a performance evaluation metric in machine learning. It is used to show the precision, recall, F1 Score, and support of your trained classification model.
F1_score :
The F1 score is a machine learning metric that can be used in classification models.
Precision_score :
The precision is the ratio tp / (tp + fp) where tp is the number of true positives and fp the number of false positives. The precision is intuitively the ability of the classifier not to label as positive a sample that is negative. The best value is 1 and the worst value is 0.
Recall_score :
Recall score is used to measure the model performance in terms of measuring the count of true positives in a correct manner out of all the actual positive values. Precision-Recall score is a useful measure of success of prediction when the classes are very imbalanced.
# Evaluating the classifier
# printing every score of the classifier
# scoring in anything
from sklearn.metrics import r2_score
li_acc = r2_score(y_test, y_predicted)*100
print("The model used is Linear Regression")
print("R2 Score is: -")
print()
print(li_acc)The model used is Linear Regression
R2 Score is: -
100.0
2. Lasso Regression
Lasso regression is also called Penalized regression method. This method is usually used in machine learning for the selection of the subset of variables. It provides greater prediction accuracy as compared to other regression models. Lasso Regularization helps to increase model interpretation.
#Using Lasso Regression
from sklearn import linear_model
la_R = linear_model.Lasso(alpha=0.1)#looking for training data
la_R.fit(X_train,y_train)Lasso(alpha=0.1)
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Lasso
Lasso(alpha=0.1)#Accuracy check of trainig data
la_R.score(X_train, y_train)0.9999999998550461# Getting kfold values
la_scores = -1 * cross_val_score(la_R,
X_train,
y_train,
cv=cv,
scoring='neg_root_mean_squared_error')
la_scoresarray([0.00210167, 0.00211321, 0.00201986, 0.00203655, 0.00219301,
0.00224288, 0.00211665, 0.00231016, 0.00222366, 0.00220961])# Mean of the train kfold scores
la_score_train = np.mean(la_scores)
la_score_train0.0021567253896499924
Prediction
Now we will perform prediction on the dataset using Lasso Regression.
# Predict the values on X_test_scaled dataset
y_predicted=la_R.predict(X_test)Evaluating all kinds of evaluating parameters.
#Accuracy check of test data
la_acc = r2_score(y_test,y_predicted)*100
print("The model used is Lasso Regression")
print("R2 Score is: -")
print()
print(la_acc)The model used is Lasso Regression
R2 Score is: -
99.99999998542768
3. Ridge Regression
Ridge regression is used when there are multiple variables that are highly correlated. It helps to prevent overfitting by penalizing the coefficients of the variables. Ridge regression reduces the overfitting by adding a penalty term to the error function that shrinks the size of the coefficients.
#Using Ridge Regression
from sklearn.linear_model import Ridge
ri_R = Ridge(alpha=1.0)#looking for training data
ri_R.fit(X_train,y_train)Ridge()
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Ridge
Ridge()#Accuracy check of trainig data
ri_R.score(X_train, y_train)0.9999999999999997# Getting kfold values
ri_scores = -1 * cross_val_score(ri_R,
X_train,
y_train,
cv=cv,
scoring='neg_root_mean_squared_error')
ri_scoresarray([3.47401962e-06, 3.41476327e-06, 3.16381057e-06, 3.32446608e-06,
3.51675952e-06, 3.67996636e-06, 3.37427759e-06, 3.57281252e-06,
3.73822604e-06, 3.66455177e-06])# Mean of the train kfold scores
ri_score_train = np.mean(ri_scores)
ri_score_train3.492365334834303e-06
Prediction
Now we will perform prediction on the dataset using Ridge Regression.
# Predict the values on X_test_scaled dataset
y_predicted=ri_R.predict(X_test)Evaluating all kinds of evaluating parameters.
#Accuracy check of test data
ri_acc = r2_score(y_test,y_predicted)*100
print("The model used is Ridge Regression")
print("R2 Score is: -")
print()
print(ri_acc)The model used is Ridge Regression
R2 Score is: -
99.99999999999997
4. RandomForestRegressor
Random Forest Regression algorithms are a class of Machine Learning algorithms that use the combination of multiple random decision trees each trained on a subset of data. The use of multiple trees gives stability to the algorithm and reduces variance. The random forest regression algorithm is a commonly used model due to its ability to work well for large and most kinds of data.
#Using Logistic Regression Algorithm to the Training Set
from sklearn.ensemble import RandomForestRegressor
rr_R = RandomForestRegressor() #Object Creation
rr_R.fit(X_train, y_train)RandomForestRegressor()
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RandomForestRegressor
RandomForestRegressor()#Accuracy check of trainig data
#Get R2 score
rr_R.score(X_train, y_train)0.9999553070560253#Accuracy of test data
rr_R.score(X_test, y_test)0.9997336147229826
Prediction
Now we will perform prediction on the dataset using Random Forest Regressor.
# Predict the values on X_test_scaled dataset
y_predicted = rr_R.predict(X_test)# Evaluating the classifier
# printing every score of the classifier
# scoring in anything
print("The model used is RandomForestRegressor")
rr_acc = r2_score(y_test, y_predicted)*100
print("R2 Score is: -")
print()
print(rr_acc)The model used is RandomForestRegressor
R2 Score is: -
99.97336147229827
Insight: -
cal_metric=pd.DataFrame([li_acc,la_acc,ri_acc,rr_acc],columns=["Score in percentage"])
cal_metric.index=['Linear Regression',
'Lasso Regression',
'Ridge Regression',
'Random Forest Regressor']
cal_metric
- As you can see with our Linear Regression(1.0000 or 100%) we are getting a better result.
- So we gonna save our model with Linear Regression Algorithm.
Step 4: Save Model
Goal:- In this step we are going to save our model in pickel format file.
import pickle
pickle.dump(li_R , open('bike_share_demand_pred_li.pkl', 'wb'))
pickle.dump(la_R , open('bike_share_demand_pred_la.pkl', 'wb'))
pickle.dump(ri_R , open('bike_share_demand_pred_ri.pkl', 'wb'))
pickle.dump(rr_R , open('bike_share_demand_pred_rr.pkl', 'wb'))import pickle
def model_prediction(features):
pickled_model = pickle.load(open('bike_share_demand_pred_li.pkl', 'rb'))
bi = str(round(pickled_model.predict(features)[0][0]))
return str(f'The bike demand are {bi}')
We can test our model by giving our own parameters or features to predict.
season = 3
holiday = 0
workingday = 1
weather = 1
temp = 33.62
atemp = 40.150
humidity = 59
windspeed = 0.0000
casual = 29
registered = 98model_prediction([[season, holiday, workingday, weather, temp, atemp, humidity, windspeed, casual, registered]])'The bike demand are 127'
Conclusion
After observing the problem statement we have build an efficient model to solve the problem. The above model helps in predicting the demand of bike sharing. The accuracy for the prediction is 100%.
Checkout whole project code here (github repo).
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