Waiter Tips Prediction
Objective:
The goal of this challenge is to build a machine learning model that predicts and find out the tips given to waiter.
Waiter ensure guests have an excellent dining experience by providing stellar customer service. They greet guests, take meal orders and ensure smooth communication between the dining area and kitchen.
Using this model, We will try to understand the various factors that determine the tip for the waiter.
Dataset: -
The dataset is available from Kaggle. It contains data about:
- total_bill: Total bill in dollars including taxes
- tip: Tip given to waiters in dollars
- sex: gender of the person paying the bill
- smoker: whether the person smoked or not
- day: day of the week
- time: lunch or dinner
- size: number of people in a table
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
import pickle
import numpy as np
import matplotlib.pyplot as plt
from sklearn.metrics import r2_score
from sklearn.linear_model import LinearRegression, Ridge, RidgeCV, Lasso, LassoCV
from sklearn.model_selection import KFold, cross_val_score, train_test_split
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/Sakshi Rohida/Desktop/deepak sir ML projects/Waiter_tips_Prediction/data/tips.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()
We will drop the columns if required.
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 244 rows and 7 columns
The df.value_counts() method counts the number of types of values a particular column contains.
df.shape(244, 7)
The df.shape method shows the shape of the dataset.
df.info()<class 'pandas.core.frame.DataFrame'>
RangeIndex: 244 entries, 0 to 243
Data columns (total 7 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 total_bill 244 non-null float64
1 tip 244 non-null float64
2 sex 244 non-null object
3 smoker 244 non-null object
4 day 244 non-null object
5 time 244 non-null object
6 size 244 non-null int64
dtypes: float64(2), int64(1), object(4)
memory usage: 13.5+ 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]total_bill 10.34
tip 1.66
sex Male
smoker No
day Sun
time Dinner
size 3
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 4
There names are as follows: ['sex', 'smoker', 'day', 'time']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 1
There names are as follows: ['size']float64_cols = ['float64']
float64_lst = list(df.select_dtypes(include=float64_cols).columns)print("Total number of float64 columns are ", len(float64_lst))
print("There name are as follow: ", float64_lst)Total number of float64 columns are 2
There name are as follow: ['total_bill', 'tip']
Step 2 Insights: -
- We have total 7 features where 2 columns are of float type, 4 columns are categorial type, and 1 is of integer type.
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()total_bill 8.902412
tip 1.383638
size 0.951100
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_dfstd_cal(df, float64_lst)
int64_cols = ['int64']
int64_lst = list(df.select_dtypes(include=int64_cols).columns)
std_cal(df,int64_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()total_bill 79.252939
tip 1.914455
size 0.904591
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, float64_lst)
var_cal(df, int64_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()total_bill 19.785943
tip 2.998279
size 2.569672
dtype: float64
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.The median will depict that the patient below median is Malignent and above that are Benign.
df.median()total_bill 17.795
tip 2.900
size 2.000
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, float64_lst)
median_cal(df, int64_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()total_bill 0
tip 0
sex 0
smoker 0
day 0
time 0
size 0
dtype: int64
As we notice that there are 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()total_bill 0
tip 0
sex 0
smoker 0
day 0
time 0
size 0
dtype: int64
As we notice that there are no nan values in our dataset.
- mean -> average value (for numerical)
- mode -> most repeated value (for categorical)
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
for value in objects_lst:
print(f"{value:{10}} {df[value].value_counts()}")sex Male 157
Female 87
Name: sex, dtype: int64
smoker No 151
Yes 93
Name: smoker, dtype: int64
day Sat 87
Sun 76
Thur 62
Fri 19
Name: day, dtype: int64
time Dinner 176
Lunch 68
Name: time, dtype: int64
- 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.
#Encoding categorical data values
from sklearn.preprocessing import LabelEncoder
labelencoder = LabelEncoder()
df['newday'] = labelencoder.fit_transform(df['day'])
df['newtime'] = labelencoder.fit_transform(df['time'])
df['sex'] = df['sex'].map({'Male':1,'Female':0})
df['newsmoker'] = labelencoder.fit_transform(df['smoker'])#After encoding or converting categorical col values into numbers
df['sex']0 0
1 1
2 1
3 1
4 0
..
239 1
240 0
241 1
242 1
243 0
Name: sex, Length: 244, dtype: int64#transform the data
df = df.drop(['smoker','time','day'],axis=1)df
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','int64']
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
We notice with the above results that we have following details:
- 3 columns are positive skewed.
- 1 column is negative skewed.
Step 3 Insights: -
With the statistical analysis we have found that the data have 5 columns with +ve skewness.
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
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
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()
features = df.columns.tolist()
features['total_bill', 'tip', 'sex', 'size', 'newday', 'newtime', 'newsmoker']sns.set(rc = {'figure.figsize':(18,10)})
sns.countplot(df['sex'])<AxesSubplot:xlabel='sex', ylabel='count'>
male — 1.0 and 0.0 — female Hence, Male’s have more ratio than femals
sns.set(rc = {'figure.figsize':(18,10)})
sns.countplot(df['newsmoker'])<AxesSubplot:xlabel='newsmoker', ylabel='count'>
More female smokers are there as per the above graph.
sns.set(rc = {'figure.figsize':(18,10)})
sns.countplot(df['newday'])<AxesSubplot:xlabel='newday', ylabel='count'>
sns.set(rc = {'figure.figsize':(18,10)})
sns.countplot(df['newtime'])<AxesSubplot:xlabel='newtime', ylabel='count'>
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.
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 0x24abc0d9df0>
Distplot Insights: -
Above is the distrution bar graphs to confirm about the statistics of the data about the skewness, the above results are:
- 3 columns are positive skewed & 2 are negative skewed.
- 1 column is added here i.e “charges” which is our target variable ~ which is also +ve skewed. In that case we’ll need to log 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.
df['tip'].skew()1.4654510370979401
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':(25,20)})
corr = df.corr().abs()
sns.heatmap(corr,annot=True)
plt.show()
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.
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()for value in features:
sns.catplot(data=df, x=value, kind="box")
#for target variable
sns.catplot(data=df, x='tip', kind='box')<seaborn.axisgrid.FacetGrid at 0x24abec16460>
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 separate 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 = 'tip'
# X will be the features
X = df.drop(target,axis=1)
#y will be the target variable
y = df[target]X.info()<class 'pandas.core.frame.DataFrame'>
RangeIndex: 244 entries, 0 to 243
Data columns (total 6 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 total_bill 244 non-null float64
1 sex 244 non-null int64
2 size 244 non-null int64
3 newday 244 non-null int32
4 newtime 244 non-null int32
5 newsmoker 244 non-null int32
dtypes: float64(1), int32(3), int64(2)
memory usage: 8.7 KBy0 1.01
1 1.66
2 3.50
3 3.31
4 3.61
...
239 5.92
240 2.00
241 2.00
242 1.75
243 3.00
Name: tip, Length: 244, dtype: float64# Check the shape of X and y variable
X.shape, y.shape((244, 6), (244,))# Reshape the y variable
y = y.values.reshape(-1,1)# Again check the shape of X and y variable
X.shape, y.shape((244, 6), (244, 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((195, 6), (49, 6), (195, 1), (49, 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 linear regression algorithms. As we know that our target variable is in continuous format so we have to apply linear regression algorithm.
Algorithms we are going to use in this step
- Linear Regression
- Lasso Regression
- Ridge Regression
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(no regularization)
Linear regression analysis is used to predict the value of a variable based on the value of another variable. The variable you want to predict is called the dependent variable. The variable you are using to predict the other variable’s value is called the independent variable. Its comes under Supervised Learning technique.
So, Linear regression predicts the output of a numerical dependent variable. Therefore the outcome must be a numerical or continuous value.
Train set cross-validation
#Using Linear Regression Algorithm to the Training Set
from sklearn.linear_model import LinearRegression
lin_R = LinearRegression() #Object Creation
lin_R.fit(X_train, y_train)LinearRegression()#Accuracy check of trainig data
#Get R2 score
lin_R.score(X_train, y_train)0.45591717043382185#Accuracy check of test data
#Get R2 score
lin_R.score(X_test, y_test)0.44413688261219275# Getting kfold values
lr_scores = -1 * cross_val_score(lin_R,
X_train,
y_train,
cv=cv,
scoring='neg_root_mean_squared_error')
lr_scoresarray([1.11333539, 0.59890732, 1.33551643, 0.74368208, 0.58207382,
0.80525485, 1.13897827, 1.53375999, 1.24943635, 1.52204404])# Mean of the train kfold scores
lr_score_train = np.mean(lr_scores)
lr_score_train1.062298854807202
Prediction
Now we will perform prediction on the dataset using Linear Regression.
# Predict the values on X_test_scaled dataset
y_predicted = lin_R.predict(X_test)Evaluating all kinds of evaluating parameters.
MAE :
MAE is a very simple metric which calculates the absolute difference between actual and predicted values.Now you have to find the MAE of your model which is basically a mistake made by the model known as an error. Now find the difference between the actual value and predicted value that is an absolute error but we have to find the mean absolute of the complete dataset.
so, sum all the errors and divide them by a total number of observations And this is MAE. And we aim to get a minimum MAE because this is a loss.
MSE:
MSE is a most used and very simple metric with a little bit of change in mean absolute error. Mean squared error states that finding the squared difference between actual and predicted value. We perform squared to avoid the cancellation of negative terms and it is the benefit of MSE.
RMSE :
As RMSE is clear by the name itself, that it is a simple square root of mean squared error.
Most of the time people use RMSE as an evaluation metric and mostly when you are working with deep learning techniques the most preferred metric is RMSE.
from sklearn.metrics import mean_absolute_error
print("MAE",mean_absolute_error(y_test,y_predicted))MAE 0.670380749646116# Evaluating
from sklearn.metrics import mean_absolute_error,accuracy_score,r2_score
print("The model used is Linear Regression")
lr_score_test = mean_absolute_error(y_test,y_predicted)
print(f"\nThe MAE is: {lr_score_test} " )The model used is Linear Regression
The MAE is: 0.670380749646116
2. Lasso Regression
Lasso regression is a type of linear regression that uses shrinkage. Shrinkage is where data values are shrunk towards a central point, like the mean. The lasso procedure encourages simple, sparse models (i.e. models with fewer parameters). This particular type of regression is well-suited for models showing high levels of muticollinearity or when you want to automate certain parts of model selection, like variable selection/parameter elimination.
L1 Regularization
Lasso regression performs L1 regularization, which adds a penalty equal to the absolute value of the magnitude of coefficients. This type of regularization can result in sparse models with few coefficients; Some coefficients can become zero and eliminated from the model. Larger penalties result in coefficient values closer to zero, which is the ideal for producing simpler models. On the other hand, L2 regularization (e.g. Ridge regression) doesn’t result in elimination of coefficients or sparse models. This makes the Lasso far easier to interpret than the Ridge.
#Using Lasso Regression Method to the Training dataset
ls_reg = LassoCV()
ls_reg = ls_reg.fit(X_train, y_train)#Accuracy check of trainig data
#Get R2 score
ls_reg.score(X_train, y_train)0.4306015461101801#Accuracy check of test data
#Get R2 score
ls_reg.score(X_test, y_test)0.547535208701841#Get kfold values
lasso_scores = -1 * cross_val_score(ls_reg,
X_train,
y_train,
cv=cv,
scoring='neg_root_mean_squared_error')
lasso_scoresarray([1.19681073, 0.52187822, 1.33521851, 0.69831021, 0.64060741,
0.85240871, 1.15793376, 1.39602974, 1.25002575, 1.49541672])# Mean of the train kfold scores
lasso_score_train = np.mean(lasso_scores)
lasso_score_train1.0544639763973147
Prediction
# Predict the values on X_test_scaled dataset
y_predicted = ls_reg.predict(X_test)# Evaluating
print("The model used is Lasso Regression")
lasso_score_test = mean_absolute_error(y_test,y_predicted)
print(f"\nThe MAE is: {lasso_score_test}")The model used is Lasso Regression
The MAE is: 0.6218431188346416
3.Ridge regression
Ridge regression is a model tuning method that is used to analyse any data that suffers from multicollinearity. This method performs L2 regularization. When the issue of multicollinearity occurs, least-squares are unbiased, and variances are large, this results in predicted values being far away from the actual values.
#Using Ridge Regression Method on Trainig Dataset
ridge_reg = RidgeCV()
ridge_reg = ridge_reg.fit(X_train,y_train)#Accuracy check of trainig data
#Get R2 score
ridge_reg.score(X_train, y_train)0.45566042047345423#Accuracy of test data
ridge_reg.score(X_test, y_test)0.4577366309784381# Get kfold values
ridge_scores = -1 * cross_val_score(ridge_reg,
X_train,
y_train,
cv=cv,
scoring='neg_root_mean_squared_error')
ridge_scoresarray([1.11761993, 0.57839652, 1.33567282, 0.73592526, 0.58328589,
0.80739468, 1.13265331, 1.49691304, 1.2458852 , 1.51831724])# Mean of the train kfold scores
ridge_score_train = np.mean(ridge_scores)
ridge_score_train1.0552063897902517
Prediction
# predict the values on X_test_scaled dataset
y_predicted = ridge_reg.predict(X_test)Evaluating all kinds of evaluating parameters.
# Evaluating
# printing every error of the regressor
from sklearn.metrics import mean_absolute_error
from sklearn.metrics import mean_squared_error
print("The model used is Linear Regression")
ridge_score_test = mean_absolute_error(y_test,y_predicted)
print(f"\nThe MAE is: {ridge_score_test}")
ridge_score_test = mean_squared_error(y_test, y_predicted)
print(f"\nThe MSE is: {ridge_score_test}")
ridge_score_test = np.sqrt(mean_squared_error(y_test, y_predicted))
print(f"\nThe RMSE is: {ridge_score_test}")The model used is Linear Regression
The MAE is: 0.6644428386520141
The MSE is: 0.6778136728680442
The RMSE is: 0.8232944023057877
Evaluation: -
test_metrics = np.array([round(lr_score_test,3),
round(lasso_score_test,3),
round(ridge_score_test,3)])
test_metrics = pd.DataFrame(test_metrics, columns=['RMSE (Test Set)'])
test_metrics.index = ['Linear Regression',
'Lasso Regression',
'Ridge Regression']
test_metrics
Insight:-
- A low RMSE value indicates that the simulated and observed data are close to each other showing a better accuracy. Thus lower the RMSE better is model performance.
- As You can see from above RMSE , we will go with Ridge Regression.(According to our dataset).
Step 4: Save Model
Goal:- In this step we are going to save our model in pickel format file.
import pickle
pickle.dump(lin_R , open('Waiter_tips_Prediction_linear.pkl', 'wb'))import pickle
pickle.dump(ls_reg , open('Waiter_tips_Prediction_lasso.pkl', 'wb'))import pickle
pickle.dump(ridge_reg , open('Waiter_tips_Prediction_ridge.pkl', 'wb'))import pickle
def model_prediction(features):
pickled_model = pickle.load(open('Waiter_tips_Prediction_linear.pkl', 'rb'))
tip = str(list(pickled_model.predict(features)))
return str(f'The amount of the tip is {tip}')
We can test our model by giving our own parameters or features to predict.
total_bill = 24.50
sex = 1.00
size = 4.00
day = 0.00
time = 1.00
smoker = 0model_prediction([[total_bill,sex,size,day,time,smoker]])'The amount of the tip is [array([3.97416925])]'
Conclusion
After observing the problem statement we have build an efficient model to overcome it. The above model helps in predicting the amount of tip given to the waiter. The accuracy for the prediction is 82.30%.
Checkout whole project code here (github repo).
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