Machine Learning Project 2: Diversity in Tech Companies Best EDA Analysis

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Introduction

Welcome to “Diversity Tech Company Best EDA,” with ML that focuses on the connection between data science and diversity in the tech industry.

Also, check Machine Learning projects:

• Machine Learning Project 1: Honda Motor Stocks best Prices analysis
• Machine Learning Project 2: Diversity in Tech Companies Best EDA Analysis
• Machine Learning Project 3: Exploring Indian Cuisine Best Analysis
• Machine Learning Project 4: Exploring Video Game Data
• Machine Learning Project 5: Best Students Performance EDA
• Machine Learning Project 6: Obesity type Best EDA and classification

In this series, we will be exploring a ML project that analyzes diversity metrics in various tech companies using advanced exploratory data analysis (EDA) techniques. Our aim is to uncover valuable insights and patterns that can help shape better practices and policies for creating inclusive work environments.

By utilizing data visualizations, statistical analyses, and machine learning models, we hope to offer a more profound understanding of the current diversity landscape in the tech industry.

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Whether you’re a data enthusiast, a tech professional, or simply passionate about diversity and inclusion, this blog provides unique perspectives and practical insights.

Machine Learning Project Source Code

# This Python 3 environment comes with many helpful analytics libraries installed
# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python
# For example, here's several helpful packages to load

import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)

# Input data files are available in the read-only "../input/" directory
# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory

import os
for dirname, _, filenames in os.walk('/kaggle/input'):
    for filename in filenames:
        print(os.path.join(dirname, filename))

# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using "Save & Run All" 
# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session

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Output: /kaggle/input/diversity-in-tech-companies/Diversity in tech companies.csv

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import tensorflow as tf
import keras
import sklearn
import os

Output:

2024-05-15 06:26:29.436227: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
2024-05-15 06:26:29.436364: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered
2024-05-15 06:26:29.602469: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered

df= pd.read_csv('/kaggle/input/diversity-in-tech-companies/Diversity in tech companies.csv')
df.head()

Output:

df.columns

Output:

Index(['Year', 'Company', 'Female %', 'Male %', '% White', '% Asian',
       '% Latino', '% Black', '% Multi', '% Other', '% Undeclared'],
      dtype='object')

df.shape

(94, 11)

df.isnull().sum()

Output:

Year 0 Company 0 Female % 0 Male % 0 % White 0 % Asian 0 % Latino 0 % Black 0 % Multi 0 % Other 1 % Undeclared 0 dtype: int64

df.Company.unique()

array(['Yahoo!', 'Google', 'Apple', 'Cisco', 'eBay', 'HP', 'Indiegogo',
       'Nvidia', 'Dell', 'Ingram Micro', 'Intel', 'Groupon', 'Amazon',
       'Etsy ', 'Microsoft', 'Salesforce', 'Pandora', 'Uber', 'Slack',
       'AirBnB ', 'Netflix', 'Yelp', 'Apple (excluding undeclared)'],
      dtype=object)

df.Year.unique()

Output: array([2018, 2017, 2016, 2015, 2014])

# Create a pivot table that counts the number of times each company appears in each year
pivot_table = df.pivot_table(index='Company', columns='Year', aggfunc='size', fill_value=0)

# Sort the years in descending order
pivot_table = pivot_table.sort_index(axis=1, ascending=False)

# Plot a stacked histogram
pivot_table.plot(kind='bar', stacked=True, figsize=(14, 8))

plt.title('Distribution of Companies Across Different Years (Descending Order)')
plt.xlabel('Company')
plt.ylabel('Count')
plt.legend(title='Year', bbox_to_anchor=(1.05, 1), loc='upper left')
plt.tight_layout()
plt.show()

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This visual representation displays how data is distributed among different companies over the course of several years. The chart showcases data from 2014 to 2018, with the years listed in descending order.

Each bar represents a company, and the various colored sections within the bars indicate the amount of data for that particular company in a specific year.

Here are some key points to note:

• The stacked columns for each company consist of different colored sections representing data from 2014 (purple), 2015 (red), 2016 (green), 2017 (orange), and 2018 (blue).
• It is evident from the chart that not every company has data for all years. For instance, Netflix and Slack did not have data for certain years.
• Most companies have data available for 2018 (blue section), while the amount of data for 2014 (purple section) is relatively low.
• The height of each bar in the chart indicates the amount of data. This allows us to observe that some companies have data for multiple years, while others only have data for a select few.

In summary, this chart provides a clear overview of how data is distributed among different companies over the years. It helps us quickly understand the completeness and coverage of the data, as some companies have data for all years while others have data for only a portion of the time period.

df = df.replace('-', 0)
df = df.replace('<1', 0.5)


percentage_columns = ['% White', '% Asian', '% Latino', '% Black', '% Multi', '% Other', '% Undeclared']
for col in percentage_columns:
    df[col] = pd.to_numeric(df[col])


df_long = pd.melt(df, id_vars=['Year', 'Company'], value_vars=percentage_columns, 
                  var_name='Race', value_name='Percentage')


g = sns.catplot(x='Year', y='Percentage', hue='Race', col='Company', col_wrap=3, 
                data=df_long, kind='bar', height=4, aspect=1.5)


g.fig.subplots_adjust(top=0.92)
g.fig.suptitle('Racial Distribution of Companies Across Different Years', fontsize=20)
plt.show()

This graph provides a visual representation of the racial makeup of various companies over the years. Each individual chart represents a specific company, and the different colored bars indicate the percentage of each race within that company for a particular year.

Upon examining the data, it is evident that the majority of companies have a significant number of White employees, as indicated by the blue bars. The information presented spans from 2014 to 2018, allowing for a comprehensive view of the racial distribution over time.

Interestingly, one company stands out from the rest in terms of diversity. Nvidia exhibits a more balanced racial distribution among its employees, with a relatively equal proportion of Whites and Asians. This sets Nvidia apart from the other companies, where Whites dominate the workforce.

import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt



df = df.replace('-', 0)
df = df.replace('<1', 0.5)


gender_columns = ['Female %', 'Male %']
for col in gender_columns:
    df[col] = pd.to_numeric(df[col])


df_long_gender = pd.melt(df, id_vars=['Year', 'Company'], value_vars=gender_columns, 
                         var_name='Gender', value_name='Percentage')


g = sns.catplot(x='Year', y='Percentage', hue='Gender', col='Company', col_wrap=3, 
                data=df_long_gender, kind='bar', height=4, aspect=1.5, margin_titles=True)


g.fig.subplots_adjust(top=0.92, hspace=0.4, wspace=0.3)

g.fig.suptitle('Gender Distribution of Companies Across Different Years', fontsize=16)
plt.show()

This chart shows the gender ratio of different companies in different years. Each sub-chart represents a company, and the different colored bars indicate the gender ratio of that company in a particular year. Below is a detailed description of this chart:

Overall Observation.

• Overall, most companies have a significantly higher percentage of male employees (Male %, orange bars) than female employees (Female %, blue bars).
• Data for each company is shown for different years (2017 and 2018).

Overall, most companies show a trend of male dominance in the gender ratio. Although the exact ratio varies from company to company, this trend is more consistent across all companies.

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Data Cleaning & Preparation EDA

# This Python 3 environment comes with many helpful analytics libraries installed
# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python
# For example, here's several helpful packages to load
import pandas as pd
df= pd.read_csv('/kaggle/input/diversity-in-tech-companies/Diversity in tech companies.csv')
df.head(5)

# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using "Save & Run All" 
# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session

#1. Check all columns
df.columns

Index(['Year', 'Company', 'Female %', 'Male %', '% White', '% Asian',
       '% Latino', '% Black', '% Multi', '% Other', '% Undeclared'],
      dtype='object')

#2.Check for missing values
print(df.isnull().sum())

Year            0
Company         0
Female %        0
Male %          0
% White         0
% Asian         0
% Latino        0
% Black         0
% Multi         0
% Other         1
% Undeclared    0
dtype: int64

# 3. Summary statistics for numerical columns
print(df.describe())

              Year   Female %     Male %    % White
count    94.000000  94.000000  94.000000  94.000000
mean   2016.106383  35.234043  64.744681  59.393617
std       1.432856   9.446426   9.464065   9.897559
min    2014.000000  16.000000  46.000000  37.000000
25%    2015.000000  29.000000  57.250000  53.000000
50%    2016.000000  33.000000  67.000000  60.000000
75%    2017.000000  42.750000  71.000000  66.500000
max    2018.000000  54.000000  84.000000  79.000000

# 4. Distribution of each numerical variable
import seaborn as sns
import matplotlib.pyplot as plt
df.hist(figsize=(8, 8))
plt.tight_layout()
plt.show()

# 5. Distribution of categorical variables
sns.countplot(x='Company', data=df)
plt.xticks(rotation=90)
plt.show()

# 7. Visualize relationships
sns.pairplot(df)
plt.show()

/opt/conda/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
  with pd.option_context('mode.use_inf_as_na', True):
/opt/conda/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
  with pd.option_context('mode.use_inf_as_na', True):
/opt/conda/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
  with pd.option_context('mode.use_inf_as_na', True):
/opt/conda/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
  with pd.option_context('mode.use_inf_as_na', True):

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# 8. Checking data type
df.dtypes

Year             int64
Company         object
Female %         int64
Male %           int64
% White          int64
% Asian         object
% Latino        object
% Black         object
% Multi         object
% Other         object
% Undeclared    object
dtype: object

percentage_columns = ['Female %', 'Male %', '% White', '% Asian', '% Latino', '% Black', '% Multi','% Other',  '% Undeclared']
for col in percentage_columns:
    if col in ['Female %', 'Male %', '% White']:
        df[col] = pd.to_numeric(df[col].replace('<', '').replace('>', '').replace('-', '0'))
    else:
        df[col] = pd.to_numeric(df[col].replace('<', '').replace('>', '').replace('-', '0').replace('', '0'), errors='coerce').fillna(0).astype(int)

df.head(10)

# Group by company and year
grouped = df.groupby(['Company', 'Year']).mean().reset_index()

# Set a color palette
palette = sns.color_palette("husl", len(grouped['Company'].unique()))

# Plotting trends for each diversity metric
for col in percentage_columns:
    plt.figure(figsize=(12, 6))
    for i, company in enumerate(grouped['Company'].unique()):
        company_data = grouped[grouped['Company'] == company]
        plt.plot(company_data['Year'], company_data[col], marker='o', label=company, color=palette[i])

    plt.title(f'Trend in {col} Across Companies Over Years')
    plt.xlabel('Year')
    plt.ylabel(f'{col}')
    plt.xticks(grouped['Year'].unique()) 
    plt.legend(bbox_to_anchor=(1, 1), loc='upper left')
    plt.grid(True)
    plt.show()

# Company Comparison for female population 
plt.figure(figsize=(12, 6))
sns.barplot(x='Company', y='Female %', data=df, hue='Year')
plt.title('Female % Across Companies')
plt.xlabel('Company')
plt.ylabel('Female %')
plt.xticks(rotation=45)
plt.legend(title='Year')
plt.show()

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# Company Comparison for black population 
plt.figure(figsize=(12, 6))
sns.barplot(x='Company', y='% Black', data=df, hue='Year')
plt.title('Black % Across Companies')
plt.xlabel('Company')
plt.ylabel('Female %')
plt.xticks(rotation=45)
plt.legend(title='Year')
plt.show()

# Overall Diversity Distribution
plt.figure(figsize=(12, 6))
sns.boxplot(data=df[percentage_columns])
plt.title('Overall Diversity Distribution')
plt.ylabel('Percentage')
plt.xticks(rotation=45)
plt.show()

# Correlation Analysis
correlation_matrix = df[percentage_columns].corr()
plt.figure(figsize=(10, 8))
sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm', square=True)
plt.title('Correlation Matrix of Diversity Metrics')
plt.show()

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# Ranking Companies
df['Total Diversity'] = df[['Female %', 'Male %', '% White', '% Asian', '% Latino', '% Black', '% Multi', '% Other', '% Undeclared']].sum(axis=1)
ranked_df = df.groupby('Company')['Total Diversity'].mean().sort_values(ascending=False).reset_index()
plt.figure(figsize=(12, 6))
sns.barplot(x='Company', y='Total Diversity', data=ranked_df)
plt.title('Ranking of Companies Based on Total Diversity')
plt.xlabel('Company')
plt.ylabel('Total Diversity')
plt.xticks(rotation=45)
plt.show()

# Company Comparison for FAANG comapnies(Facebook, Amazon, Apple, Netflix, and Google) 
selected_companies = ['Amazon','Apple','Netflix','Google']
selected_df = df[df['Company'].isin(selected_companies)]
plt.figure(figsize=(12, 6))
sns.lineplot(x='Year', y='Female %', hue='Company', data=selected_df)
plt.title('Trend in Female % Among FAANG Companies (2014-2018)')
plt.xlabel('Year')
plt.xticks(grouped['Year'].unique())  # Set x-axis ticks to be the unique years
plt.ylabel('Female %')
plt.legend(title='Company')
plt.grid(True)
plt.show()

/opt/conda/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
  with pd.option_context('mode.use_inf_as_na', True):
/opt/conda/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
  with pd.option_context('mode.use_inf_as_na', True):
/opt/conda/lib/python3.10/site-packages/seaborn/_oldcore.py:1075: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.
  data_subset = grouped_data.get_group(pd_key)
/opt/conda/lib/python3.10/site-packages/seaborn/_oldcore.py:1075: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.
  data_subset = grouped_data.get_group(pd_key)
/opt/conda/lib/python3.10/site-packages/seaborn/_oldcore.py:1075: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.
  data_subset = grouped_data.get_group(pd_key)
/opt/conda/lib/python3.10/site-packages/seaborn/_oldcore.py:1075: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.
  data_subset = grouped_data.get_group(pd_key)

# Melt the DataFrame to have a single 'Ethnicity' column
melted_df = df.melt(id_vars=['Year', 'Company'], var_name='Ethnicity', value_name='Percentage')
# Plotting
plt.figure(figsize=(12, 6))
sns.lineplot(x='Year', y='Percentage', hue='Ethnicity', data=melted_df, marker='o')
plt.title('Rise of Multiple Ethnicities in FAANG Companies (2014-2018)')
plt.xlabel('Year')
plt.xticks(grouped['Year'].unique())  # Set x-axis ticks to be the unique years
plt.ylabel('Percentage')
plt.grid(True)
plt.show()

/opt/conda/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
  with pd.option_context('mode.use_inf_as_na', True):
/opt/conda/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
  with pd.option_context('mode.use_inf_as_na', True):
/opt/conda/lib/python3.10/site-packages/seaborn/_oldcore.py:1075: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.
  data_subset = grouped_data.get_group(pd_key)
/opt/conda/lib/python3.10/site-packages/seaborn/_oldcore.py:1075: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.
  data_subset = grouped_data.get_group(pd_key)
/opt/conda/lib/python3.10/site-packages/seaborn/_oldcore.py:1075: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.
  data_subset = grouped_data.get_group(pd_key)
/opt/conda/lib/python3.10/site-packages/seaborn/_oldcore.py:1075: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.
  data_subset = grouped_data.get_group(pd_key)
/opt/conda/lib/python3.10/site-packages/seaborn/_oldcore.py:1075: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.
  data_subset = grouped_data.get_group(pd_key)
/opt/conda/lib/python3.10/site-packages/seaborn/_oldcore.py:1075: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.
  data_subset = grouped_data.get_group(pd_key)
/opt/conda/lib/python3.10/site-packages/seaborn/_oldcore.py:1075: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.
  data_subset = grouped_data.get_group(pd_key)
/opt/conda/lib/python3.10/site-packages/seaborn/_oldcore.py:1075: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.
  data_subset = grouped_data.get_group(pd_key)
/opt/conda/lib/python3.10/site-packages/seaborn/_oldcore.py:1075: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.
  data_subset = grouped_data.get_group(pd_key)
/opt/conda/lib/python3.10/site-packages/seaborn/_oldcore.py:1075: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.
  data_subset = grouped_data.get_group(pd_key)

Shifting Paradigms: Diversity Trends in Tech

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import warnings
warnings.filterwarnings('ignore')

data = pd.read_csv("/kaggle/input/diversity-in-tech-companies/Diversity in tech companies.csv")

data.head()

plt.figure(figsize=(10, 6))
sns.lineplot(data=data, x='Year', y='Female %', hue='Company', marker='o')
plt.title('Gender Distribution Over the Years')
plt.xlabel('Year')
plt.ylabel('Female %')
plt.legend(title='Company', bbox_to_anchor=(1, 1))
plt.show()

plt.figure(figsize=(10, 6))
sns.boxplot(data=data, x='Company', y='% White')
plt.title('Ethnic Diversity Comparison Between Companies')
plt.xlabel('Company')
plt.ylabel('% White')
plt.xticks(rotation=90)
plt.show()

ethnic_groups = ['% Asian', '% Latino', '% Black', '% Multi', '% Other', '% Undeclared']
plt.figure(figsize=(12, 8))
for group in ethnic_groups:
    sns.lineplot(data=data, x='Year', y=group, label=group)
plt.title('Ethnic Diversity Distribution Over the Years')
plt.xlabel('Year')
plt.ylabel('Percentage')
plt.legend(title='Ethnicity')
plt.show()

plt.figure(figsize=(10, 6))
sns.barplot(data=data, x='Company', y='Female %', hue='Year')
plt.title('Gender Distribution by Company')
plt.xlabel('Company')
plt.ylabel('Female %')
plt.legend(title='Year', bbox_to_anchor=(1, 1))
plt.xticks(rotation=90)
plt.show()

What is the best automated EDA?

Automated EDA tools are your data’s best pals, making sense of numbers effortlessly. Picture a genius friend who uncovers patterns, spots outliers, and transforms your data into a compelling story.

Here’s the scoop:

• Pandas Profiling: A timeless tool that gives your data a thorough check-up. It delivers a detailed report with summaries and visualizations, perfect for a quick data overview.
• Sweetviz: This tool is as sweet as its name suggests. It creates stunning visualizations with ease, like a data Instagram feed highlighting key insights in vibrant colors.
• D-Tale: Imagine your data coming alive with this interactive tool. It’s like having a data whisperer that lets you explore, filter, and plot data without coding. Ideal for non-coders or those seeking a coding break.
• AutoViz: Need speed? AutoViz is your solution. It instantly visualizes datasets with a single line of code, no need for endless adjustments. Perfect for quick insights and overviews.
• Exploratory: The Swiss Army knife of EDA tools, offering simple summaries to advanced ML models in a user-friendly interface. Collaborative features allow you to share findings effortlessly.

These tools each bring something unique to the table, like different ice cream flavors. Give them a try and discover which one makes you say, “Wow, this is amazing!”

Hope that helps! If you need more deets, just holler! 🚀

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What is EDA analysis in machine learning?

Exploratory Data Analysis (EDA) is similar to being a detective for data. Instead of jumping straight into complex machine learning tasks, it’s important to familiarize yourself with the data first.

It involves exploring and comprehending your dataset, just like getting to know someone new. Here’s how it unfolds:

See? Easy peasy, just like planning a fun get-together. Each step gets you closer to understanding your data and making it work for you. 🎉📊🔍

Do a project on machine learning?

Welcome to the Movie Magic Predictor project! 🍿🎬 Let’s explore the fascinating world of machine learning to forecast the success of upcoming movies. Picture having a magical crystal ball that reveals which movies will be hits and which ones might not do so well!

Objective

Our aim is straightforward: to develop a machine learning model that can anticipate the box office performance of movies by considering various factors like genre, cast, budget, and more.

Through analyzing past movie data and deriving valuable insights, we strive to create a predictive model that can assist filmmakers, studios, and investors in making well-informed choices.

Dataset

We will be delving into a comprehensive dataset that contains details about thousands of movies released in recent decades.

This dataset comprises information such as movie title, release date, genre, cast and crew, budget, box office earnings, and critical reception.

Code

# Importing necessary libraries
import pandas as pd

# Step 1: Data Collection
# Load the movie dataset
movie_data = pd.read_csv('movie_dataset.csv')

# Display the first few rows of the dataset to understand its structure
print("First few rows of the dataset:")
print(movie_data.head())

# Step 2: Exploratory Data Analysis (EDA)
# Get basic information about the dataset
print("\nDataset information:")
print(movie_data.info())

# Summary statistics of numerical features
print("\nSummary statistics of numerical features:")
print(movie_data.describe())

# Summary of categorical features
print("\nSummary of categorical features:")
print(movie_data.describe(include=['object']))

# Check for missing values
print("\nMissing values in the dataset:")
print(movie_data.isnull().sum())

# Visualize the distribution of target variable (box office earnings)
import matplotlib.pyplot as plt

plt.figure(figsize=(10, 6))
plt.hist(movie_data['BoxOffice'], bins=20, color='skyblue', edgecolor='black')
plt.title('Distribution of Box Office Earnings')
plt.xlabel('Box Office Earnings')
plt.ylabel('Frequency')
plt.show()

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Which ML project is best?

JustiScan:
Objective: Investigating social media and news for injustices, highlighting disparities and advocating for fairness.
Impact: Serving as a champion of equality, bringing attention to issues that require attention for the betterment of society.

Each of these initiatives plays a crucial role in addressing significant challenges and making a positive impact on the world. Which one resonates with you the most?

Conclusion

In conclusion, our exploration of diversity within tech companies has been eye-opening and empowering. Through the lens of exploratory data analysis (EDA), we have delved deep into the numbers, uncovering valuable insights that shed light on the landscape of diversity in the tech industry.

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From analyzing demographic trends to scrutinizing hiring practices, our EDA journey has revealed both successes and challenges. We have celebrated companies that are making progress in fostering inclusive cultures, while also recognizing areas where improvement is necessary.

However, our work does not end here. With the power of data in our hands, we have the opportunity to drive meaningful change. By continuing to advocate for diversity, equity, and inclusion in the tech sector, we can shape a future where everyone is included.

As we conclude this chapter of our machine learning project, let’s carry forward the lessons we have learned and the insights we have gained. Let’s strive to build a tech industry that truly represents the diverse tapestry of humanity, where diversity is not just a buzzword but a fundamental pillar of success.

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Together, we can create a future where diversity is not an afterthought, but the driving force behind innovation, creativity, and progress. Thank you for joining us on this journey, and here’s to a brighter, more inclusive future for all in the tech industry.

Keep coding, keep advocating, and keep pushing for change. The future is ours to shape.