Profit Prediction Model
Objective:
A company should always set a goal that should be achievable, otherwise, employees will not be able to work to their best potential if they find that the goal set by the company is unachievable. Though, The profit earned by a company for a particular period depends on several factors.
The goal of this challenge is to build a machine learning model to predict the profit of a company for a particular period with a dataset that contains historical data about the profit generated by the company.
Dataset:
The dataset used in this model is publicly available and free to use.
Attribute Information:
- R&D spend
- Administration cost
- Marketing Spend
- State of operation
- Profit
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/Profit_Prediction_Model/data/Profit prediction.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()
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.
Why we drop column?
By analysing the first five rows we found that there is a column we need to drop in the dataset.
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 50 rows and 5 columns
By analysing the problem statement and the dataset, we get to know that the target variable is “diagnosis” column which says if the cancer is (M = malignant) or (B = benign). 1 means the cancer is malignant and 0 means the cancer is benign.
df['Profit'].value_counts()192261.83 1
89949.14 1
105008.31 1
103282.38 1
101004.64 1
99937.59 1
97483.56 1
97427.84 1
96778.92 1
96712.80 1
96479.51 1
90708.19 1
81229.06 1
191792.06 1
81005.76 1
78239.91 1
77798.83 1
71498.49 1
69758.98 1
65200.33 1
64926.08 1
49490.75 1
42559.73 1
35673.41 1
105733.54 1
107404.34 1
108552.04 1
108733.99 1
191050.39 1
182901.99 1
166187.94 1
156991.12 1
156122.51 1
155752.60 1
152211.77 1
149759.96 1
146121.95 1
144259.40 1
141585.52 1
134307.35 1
132602.65 1
129917.04 1
126992.93 1
125370.37 1
124266.90 1
122776.86 1
118474.03 1
111313.02 1
110352.25 1
14681.40 1
Name: Profit, dtype: int64
The df.value_counts() method counts the number of types of values a particular column contains.
df.shape(50, 5)
The df.shape method shows the shape of the dataset.
df.info()<class 'pandas.core.frame.DataFrame'>
RangeIndex: 50 entries, 0 to 49
Data columns (total 5 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 R&D Spend 50 non-null float64
1 Administration 50 non-null float64
2 Marketing Spend 50 non-null float64
3 State 50 non-null object
4 Profit 50 non-null float64
dtypes: float64(4), object(1)
memory usage: 2.1+ 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]R&D Spend 162597.7
Administration 151377.59
Marketing Spend 443898.53
State California
Profit 191792.06
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.
df.rename(columns={"R&D Spend": "R&D_Spend", "Marketing Spend": "Marketing_Spend"})
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: ['State']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 0
There names are as follows: []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 4
There name are as follow: ['R&D Spend', 'Administration', 'Marketing Spend', 'Profit']#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 50 rows and 5 columns
Step 2 Insights: -
- There are 4 columns that are of float 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()R&D Spend 45902.256482
Administration 28017.802755
Marketing Spend 122290.310726
Profit 40306.180338
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()R&D Spend 2.107017e+09
Administration 7.849973e+08
Marketing Spend 1.495492e+10
Profit 1.624588e+09
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 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()R&D Spend 73721.6156
Administration 121344.6396
Marketing Spend 211025.0978
Profit 112012.6392
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.
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()R&D Spend 0
Administration 0
Marketing Spend 0
State 0
Profit 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()R&D Spend 0
Administration 0
Marketing Spend 0
State 0
Profit 0
dtype: int64
As we notice that there are no nan values in our dataset.
Another way to remove null and nan values is to use the method “df.dropna(inplace=True)”.
- 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.
#Before Encoding
df['State']0 New York
1 California
2 Florida
3 New York
4 Florida
5 New York
6 California
7 Florida
8 New York
9 California
10 Florida
11 California
12 Florida
13 California
14 Florida
15 New York
16 California
17 New York
18 Florida
19 New York
20 California
21 New York
22 Florida
23 Florida
24 New York
25 California
26 Florida
27 New York
28 Florida
29 New York
30 Florida
31 New York
32 California
33 Florida
34 California
35 New York
36 Florida
37 California
38 New York
39 California
40 California
41 Florida
42 California
43 New York
44 California
45 New York
46 Florida
47 California
48 New York
49 California
Name: State, dtype: object#Encoding categorical data values
from sklearn.preprocessing import LabelEncoder
le = LabelEncoder()
df.State = le.fit_transform(df.State)#After encoding or converting categorical col values into numbers
df['State'].unique()array([2, 0, 1])
- 0 ~ New York
- 1 ~ California
- 2 ~ Florida
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 float64_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
We notice with the above results that we have following details:
- 2 columns are positive 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
Step 4: Data Exploration
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=(8,8))
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.
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 0x24c7c75d640>
Distplot Insights: -
Above is the distrution bar graphs to confirm about the statistics of the data about the skewness, the above results are:
- 30 columns are positive skewed
- 1 column is added here i.e diagnosis 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.
#+ve skewed
df['Profit'].skew()0.023291019769116614
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':(7,7)})
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.
# Create correlation matrix
corr_matrix = df.corr().abs()
# Select upper triangle of correlation matrix
upper = corr_matrix.where(np.triu(np.ones(corr_matrix.shape), k=1).astype(np.bool_))
# Find index of feature columns with correlation greater than 0.8
to_drop = [column for column in upper.columns if any(upper[column] > 0.8)]to_drop['Profit']
These are the columns that we have to drop.
#Here we are droping those columns
df.drop(columns = ['Profit'])
df.head()
df.info()<class 'pandas.core.frame.DataFrame'>
RangeIndex: 50 entries, 0 to 49
Data columns (total 5 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 R&D Spend 50 non-null float64
1 Administration 50 non-null float64
2 Marketing Spend 50 non-null float64
3 State 50 non-null int32
4 Profit 50 non-null float64
dtypes: float64(4), int32(1)
memory usage: 1.9 KB
Here, we are checking which columns we have after “to-drop”.
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 = ['R&D Spend','Administration','Marketing Spend','State','Profit']#for target variable
sns.catplot(data=df, x='Profit', kind='box')<seaborn.axisgrid.FacetGrid at 0x24c7c903100>
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.
6. Bar Plot
sns.set(rc = {'figure.figsize':(45,25)})
sns.barplot(x='Administration',y='Profit', data=df,
hue='State')
plt.show()
This is how columns relate to each other.
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 = 'Profit'
# 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: 50 entries, 0 to 49
Data columns (total 4 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 R&D Spend 50 non-null float64
1 Administration 50 non-null float64
2 Marketing Spend 50 non-null float64
3 State 50 non-null int32
dtypes: float64(3), int32(1)
memory usage: 1.5 KBy0 192261.83
1 191792.06
2 191050.39
3 182901.99
4 166187.94
5 156991.12
6 156122.51
7 155752.60
8 152211.77
9 149759.96
10 146121.95
11 144259.40
12 141585.52
13 134307.35
14 132602.65
15 129917.04
16 126992.93
17 125370.37
18 124266.90
19 122776.86
20 118474.03
21 111313.02
22 110352.25
23 108733.99
24 108552.04
25 107404.34
26 105733.54
27 105008.31
28 103282.38
29 101004.64
30 99937.59
31 97483.56
32 97427.84
33 96778.92
34 96712.80
35 96479.51
36 90708.19
37 89949.14
38 81229.06
39 81005.76
40 78239.91
41 77798.83
42 71498.49
43 69758.98
44 65200.33
45 64926.08
46 49490.75
47 42559.73
48 35673.41
49 14681.40
Name: Profit, dtype: float64# Check the shape of X and y variable
X.shape, y.shape((50, 4), (50,))# Reshape the y variable
y = y.values.reshape(-1,1)# Again check the shape of X and y variable
X.shape, y.shape((50, 4), (50, 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((40, 4), (10, 4), (40, 1), (10, 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 classification algorithms. As we know that our target variable is in discrete format so we have to apply classification algorithm. Target variable is a category like filtering.In our dataset we have the outcome variable or Dependent variable i.e Y having only two set of values, either M (Malign) or B(Benign). So we will use Classification 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
Linear regression attempts to model the relationship between two variables by fitting a linear equation to observed data. One variable is considered to be an explanatory variable, and the other is considered to be a dependent variable.
A linear regression line has an equation of the form Y = a + bX, where X is the explanatory variable and Y is the dependent variable. The slope of the line is b, and a is the intercept (the value of y when x = 0).
Train set cross-validation
#Using Linear Regression
from sklearn.linear_model import LinearRegression
model = LinearRegression()
model.fit(X_train, y_train)
model.score(X_test, y_test)0.9000614254946402#Accuracy check of trainig data
from sklearn.metrics import r2_score
#Get R2 score
model.score(X_train, y_train)0.9535928780839645#Accuracy of test data
model.score(X_test, y_test)0.9000614254946402# Getting kfold values
lg_scores = -1 * cross_val_score(model,
X_train,
y_train,
cv=cv,
scoring='neg_root_mean_squared_error')
lg_scoresarray([ 8561.8251955 , 10183.74126448, 11027.10822524, 5949.14921324,
20345.22534321, 7264.73433981, 7654.76044164, 3628.01275154,
9420.64417692, 6739.85487021])# Mean of the train kfold scores
lg_score_train = np.mean(lg_scores)
lg_score_train9077.505582178735
Prediction
Now we will perform prediction on the dataset using Logistic Regression.
# Predict the values on X_test_scaled dataset
y_predicted = model.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 classification_report
from sklearn.metrics import f1_score, accuracy_score, precision_score,recall_score
from sklearn.metrics import confusion_matrix
print("The model used is Linear Regression")
rg = r2_score(y_test,y_predicted)*100
print("\nThe accuracy is: {}".format(rg))The model used is Linear Regression
The accuracy is: 90.00614254946402
2. Lasso Regression
Lasso regression algorithm is defined as a regularization algorithm that assists in the elimination of irrelevant parameters, thus helping in the concentration of selection and regularizes the models.
#Using Lasso Regression
from sklearn import linear_model
clf = linear_model.Lasso(alpha=0.1)#looking for training data
clf.fit(X_train,y_train)Lasso(alpha=0.1)#Accuracy check for training data
clf.score(X_train,y_train)0.9535928780746373y_predicted1 = clf.predict(X_test)#Accuracy check of test data
lg = r2_score(y_test,y_predicted1)*100
lg90.00614657116638#Get kfold values
Nn_scores = -1 * cross_val_score(clf,
X_train,
y_train,
cv=cv,
scoring='neg_root_mean_squared_error')
Nn_scoresarray([ 8561.81722141, 10183.69811212, 11027.11015238, 5949.0534489 ,
20345.2030761 , 7264.65480812, 7654.67169038, 3627.97819362,
9420.58884219, 6739.8083833 ])# Mean of the train kfold scores
Nn_score_train = np.mean(Nn_scores)
Nn_score_train9077.458392853954print("The model used is Linear Regression")
lg = r2_score(y_test,y_predicted)*100
print("\nThe accuracy is: {}".format(lg))The model used is Linear Regression
The accuracy is: 90.00614254946402
3. Ridge Regression
#Using Ridge Regression
from sklearn.linear_model import Ridge
rig = Ridge(alpha=1.0)
rig.fit(X_train, y_train)Ridge()#Accuracy check of trainig data
#Get R2 score
rig.score(X_train, y_train)0.9535928779315187# Get kfold values
Dta_scores = -1 * cross_val_score(rig,
X_train,
y_train,
cv=cv,
scoring='neg_root_mean_squared_error')
Dta_scoresarray([ 8561.4496701 , 10179.07140564, 11027.1614498 , 5937.53379051,
20342.37975438, 7253.18907056, 7644.95280762, 3627.13640031,
9413.73714598, 6737.17128835])# Mean of the train kfold scores
Dta_score_train = np.mean(Dta_scores)
Dta_score_train9072.378278326467
Prediction
# predict the values on X_test_scaled dataset
y_predicted = rig.predict(X_test)Evaluating all kinds of evaluating parameters.
from sklearn.metrics import classification_report
from sklearn.metrics import f1_score, accuracy_score, precision_score,recall_score
from sklearn.metrics import confusion_matrix
print("The model used is Ridge Regression")
r_acc = r2_score(y_test, y_predicted)*100
print("\nThe accuracy is {}".format(r_acc))The model used is Ridge Regression
The accuracy is 90.00615874908104
Insight
cal_metric=pd.DataFrame([rg,lg,r_acc],columns=["Profit"])
cal_metric.index=['Linear Regression',
'Lasso Regression','Ridge Regression']
cal_metric
- As you can see with our Random Forest Model(0.9298 or 92.98%) we are getting a better result even for the recall (0.9069 or 90.69%) which is the most tricky part.
- So we gonna save our model with Random Forest Algorithm
Step 4: Save Model
Goal:- In this step we are going to save our model in pickel format file.
import pickle
pickle.dump(model , open('Profit_Prediction_LinearRegresssion.pkl', 'wb'))
pickle.dump(clf , open('Profit_Prediction_LassoRegresssion.pkl', 'wb'))
pickle.dump(rig , open('Profit_Prediction_RidgeRegresssion.pkl', 'wb'))import pickle
def model_prediction(features):
pickled_model = pickle.load(open('Profit_Prediction_LinearRegresssion.pkl', 'rb'))
Profit = str(list(pickled_model.predict(features)))
return str(f'The Profit is {Profit}')df.head()
We can test our model by giving our own parameters or features to predict.
RD_Spend = 165589.12
Administration = 118521.50
Marketing_Spend = 653468.10
State = 1model_prediction([[RD_Spend, Administration, Marketing_Spend, State]])'The Profit is [array([199542.52173178])]'
Conclusion
After observing the problem statement we have build an efficient model to overcome it. The above model helps to predict the profit. The accuracy for the prediction is 90.00% and it signifies the accurate prediction of the profit.
Checkout whole project code here (github repo).
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