Coursework 2

Name: Mohamad Zgheib (H00456590)

Date: 18-03-2024

Introduction

This report analyzes a dataset that stems from a company's sales records. This is a comprehensive report which includes several steps aiming to visualize and interpret the dataset on hand. It gives insights, studies relationships, and implements different modeling methods which aim to further explain, examine, and recommend different strategies to enhance the overall state of the company.

Importing the Data

import pandas as pd data = pd.read_csv('dataset_Superstore.csv') data

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Cleaning the Data

# Check missing values data.isnull().sum()

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No missing values are found in this dataset.

# Detect outliers data.describe()

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A few outliers can be seen:

1. The maximum value of the quantity column is 10000. This is significantly greater than the 75th percentile value, which is 5. 2. Also, the maximum value of the profit column is 8399.976. This is much greater than the 75% percentile value which is 29.364

These are just a few examples of the several outliers found.

We will perform a z-score normalization to eliminate the outliers. We will eliminate values that are more than 3 standard deviations away from the mean.

from scipy.stats import zscore # Applying zscore directly in the condition for filtering outliers data['Sales_zscore'] = zscore(data['Sales']) data['Profit_zscore'] = zscore(data['Profit']) # Filtering out rows where the absolute zscore is less than 3 for both Sales and Profit data_no_outliers = data[(data['Sales_zscore'].abs() < 3) & (data['Profit_zscore'].abs() < 3)].drop(columns=['Sales_zscore', 'Profit_zscore']) data_no_outliers.describe()

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In this new 'data_no_outliers' data frame, outliers that are 3 standard deviations away from the mean were removed. The dataset without outliers has 9829 rows.

Calculating the Summary Statistics

data_no_outliers.describe()

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Plotting the Data

import matplotlib.pyplot as plt # Bar charts for Segment, Shipmode, and Category fig, axs = plt.subplots(2, 2, figsize=(14, 14)) # Bar plot for Segment data['Segment'].value_counts().plot(kind='bar', ax=axs[0, 1], color='teal') axs[0, 1].set_title('Segment') axs[0, 1].tick_params(axis='x', rotation=45) # Bar plot for Ship Mode data['Ship Mode'].value_counts().plot(kind='bar', ax=axs[0, 0], color='teal') axs[0, 0].set_title('Ship Mode') axs[0, 0].tick_params(axis='x', rotation=45) # Bar plot for Category data['Category'].value_counts().plot(kind='bar', ax=axs[1, 1], color='teal') axs[1, 1].set_title('Category') axs[1, 1].tick_params(axis='x', rotation=45) plt.tight_layout() plt.show()

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The graphical analysis reveals:

Ship Mode: The dominant choice for shipping is Standard Class, with Same Day shipping being the least favored. This preference can be due to various reasons, including cost considerations. Standard shipping often presents a more cost-effective option compared to the expenses associated with the speed and logistics required for delivering orders on the same day.

Segment: Orders are mostly placed by the Consumer segment. Corporate and Home Office segments come in second and third place. This trend might be explained by the fact that consumers, who make up a large segment of the market, typically purchase items in smaller quantities but with greater frequency. In contrast, home offices and businesses are more inclined to place bulk orders, less often.

Category: The category with the most orders is Office Supplies, followed by Furniture and Technology. The high demand for office supplies is likely due to their quick consumption rate. These supplies include pens, papers, and other gadgets. On the other hand, furniture and technology items are purchased less as they are considered durable items.

Following this, we'll proceed to create scatter plots of 'Sales' vs 'Profit' as well as 'Quantity' vs 'Discount'.

import seaborn as sns import matplotlib.pyplot as plt # Setup for scatter plots fig, axs = plt.subplots(2, 1, figsize=(10, 12)) # Scatter plot for Sales vs Profit sns.scatterplot(x='Sales', y='Profit', data=data_no_outliers, ax=axs[0]) axs[0].set_title('Sales vs Profit') # Scatter plot for Quantity vs Discount sns.scatterplot(x='Quantity', y='Discount', data=data_no_outliers, ax=axs[1]) axs[1].set_title('Quantity vs Discount') plt.tight_layout() plt.show()

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Sales vs. Profit: A slightly positive correlation is found, however it is considered a weak one. Transactions with large sales don't always correlate with big profits. There are several scenarios where, despite increased sales, profits remain low or even negative. This situation could arise from varying costs of goods sold or substantial discounts applied.

Quantity vs. Discount: No correlation is seen between the two variables. This implies that the volume of products sold doesn't directly impact the level of discount offered. Moreover, it could be said that purchasing in large amounts does not necessarily guarantee a larger discount for the customer.

Modeling strategy

Utilizing a decision tree model stands out as an effective approach for identifying the primary influences on profit. This choice is due to the model's capacity to detect non-linear associations, an essential feature given that the relationship among 'Sales', 'Quantity', 'Discount', and 'Profit' could not be linear. Moreover, decision trees typically do not require preprocessing or implementing assumptions. Hence, this modeling strategy is great for this scenario.

from sklearn.model_selection import train_test_split from sklearn.tree import DecisionTreeRegressor # features and target variable selection X = data_no_outliers[['Sales', 'Quantity', 'Discount']] y = data_no_outliers['Profit'] # train and test sets X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) # Initialize and train Decision Tree Regressor dt = DecisionTreeRegressor(random_state=42) dt.fit(X_train, y_train) # Extract and sort feature importances dt_ft_imp = pd.DataFrame({'Features': X.columns, 'Importances': dt.feature_importances_}) \ .sort_values(by='Importances', ascending=False) # view sorted feature importances print(dt_ft_imp)

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The Decision Tree Regressor model shows that 'Sales' significantly influences 'Profit', having an importance rating near 0.54. 'Discount' comes next, with an importance rate close to 0.39, whereas 'Quantity' has the smallest influence, with an importance of 0.07.

This suggests that generating more profits might be best achieved through enhancing sales and applying strategic discounts. Implementing this strategy is better than increasing the number of items sold, as quantity has the least effect on profits.

In conclusion, based on these findings, it is recommended that the company prioritize sales improvement and discount strategies to become more profitable.

References

Géron, A. (2019). Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems (2nd ed.). O'Reilly Media.

Müller, A. C., & Guido, S. (2016). Introduction to Machine Learning with Python: A Guide for Data Scientists. O'Reilly Media.

Chopra, S. (2019). Supply Chain Management: Strategy, Planning, and Operation (7th ed.). Pearson.

.css-15w88e5{color:var(--chakra-colors-fg-neutral-primary);font-weight:inherit;letter-spacing:-0.09px;}Coursework 2

Introduction

Importing the Data

Cleaning the Data

Calculating the Summary Statistics

Plotting the Data

Modeling strategy

References

Coursework 2