PYTHON DATA ANALYSIS

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Movie DataSet Exploratory Data Analysis (EDA)

Introduction

This project focuses on conducting an in-depth Exploratory Data Analysis (EDA) on the Movie dataset to extract valuable insights and trends.

• Main File: Movie_EDA.ipynb

Analysis Objectives

1. Data Understanding: Gain a comprehensive understanding of the dataset's structure, contents, and attributes.
2. Data Cleaning: Implement data cleaning techniques to handle missing values, ensure data integrity, and prepare the dataset for analysis.
3. Exploratory Data Analysis (EDA): Utilize various statistical and visualization methods to explore relationships between features, identify patterns, and extract meaningful insights.
4. Insights Generation: Generate actionable insights and recommendations based on the findings from the EDA process.

Methodology

1. Data Preparation:
   • Imported necessary libraries including pandas, numpy, matplotlib, and seaborn to facilitate data manipulation and visualization.
   • Loaded the dataset using pd.read_csv() function with appropriate encoding to read the CSV files into pandas DataFrame.
   • Inspected the initial rows of the dataset using .head() method to understand its structure and content.
   • Reviewed the columns and their data types using .columns and .info() methods to identify potential issues.
   • Calculated basic statistical metrics for numerical features using .describe() method to gain insights into the data distribution.
2. Data Cleaning:
   • Identified missing values using .isnull().sum() and addressed them appropriately to ensure data completeness.
   • Visualized missing values using a heatmap with sns.heatmap() to identify patterns and understand the extent of missingness.
   • Removed rows with missing values using .dropna() method, considering the impact on the analysis and the dataset's integrity.
   • Standardized the format of certain columns (e.g., 'year', 'runtime') to maintain consistency and facilitate analysis.
3. Exploratory Data Analysis:
   • Highest Rated Movies Analysis: Utilized .groupby() and .sort_values() functions to identify movies with the highest ratings for each year.
   • Highest Voted Movies Analysis: Employed .groupby(), .sort_values(), and .iloc[] methods to extract movies with the highest votes for each year.
   • Relationship Visualization: Created scatter plots using plt.scatter() to visualize the relationship between votes, gross revenue, and rating, exploring potential correlations.
   • Genre Analysis: Investigated popular genres for each year using .groupby() and .idxmax() to identify the genre with the highest count.
   • Revenue Analysis: Utilized .groupby() and .sort_values() to determine movies with the highest and lowest gross revenue for each year.
   • Average Duration Analysis: Calculated the average movie duration for each year using .groupby() and .mean() functions.
   • Director Analysis: Examined the director with the highest number of movies using .groupby() and .idxmax() to identify the most prolific director.
   • Rating Distribution Across Genre: Visualized the distribution of movie ratings across different genres using .groupby() and sns.barplot() functions.
   • Top Rated Movies Across Genre: Identified top-rated movies across genres using .groupby(), .sort_values(), and .iloc[] methods.
   • Rating Trends Over Years: Plotted a line graph to analyze the change in movie ratings over the years using .groupby() and sns.lineplot().
   • Rating Trends Across Genre Over Years: Created line graphs to explore how movie ratings change over the years across different genres using .groupby() and sns.lineplot().
   • Top Stars Analysis: Determined the top stars appearing in the most movies using .value_counts() and sns.barplot().
   • Revenue Trends Analysis: Examined the relationship between years and gross revenue using boxplots with sns.boxplot().
   • Runtime vs. Average Revenue Analysis: Investigated the relationship between runtime and average revenue using line graphs with sns.lineplot().

Key Findings

1. Rating Distribution: The majority of movies tend to fall within a moderate rating range, with only a few outliers at the extreme ends.
2. Genre Preferences: Certain genres exhibit consistent popularity over the years, while others experience fluctuations, indicating changing audience preferences.
3. Revenue Trends: Gross revenue demonstrates an overall increasing trend over the years, with occasional fluctuations influenced by various factors such as genre, star cast, and director.
4. Director Influence: Certain directors consistently produce successful movies, contributing significantly to the industry's success.
5. Runtime Impact: The duration of movies varies widely, with some genres favoring longer runtimes to accommodate intricate plotlines, while others prefer shorter durations for increased viewer engagement.

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Zomato DataSet Exploratory Data Analysis (EDA)

Introduction

This project focuses on conducting an in-depth Exploratory Data Analysis (EDA) on the Zomato dataset.

• main file: Zomato EDA.ipynb

Analysis Objectives

1. Data Understanding: Gain insights into the structure and content of the dataset.
2. Data Cleaning: Handle missing values and ensure data integrity.
3. Exploratory Data Analysis: Analyze the numerical and categorical variables, explore relationships between features, and visualize key trends.
4. Insights Generation: Draw actionable insights and recommendations based on the analysis.

Methodology

1. Data Preparation:
   • Imported necessary libraries including pandas, numpy, matplotlib, and seaborn.
   • Loaded the dataset using pd.read_csv() function with encoding.
   • Inspected the first few rows of the dataset using .head() to understand its structure.
   • Checked the columns and data types using .columns and .info() methods.
   • Explored basic statistics of numerical features using .describe().
2. Data Cleaning:
   • Checked for missing values using .isnull().sum() and identified columns with missing values.
   • Visualized missing values using a heatmap with sns.heatmap() to identify patterns.
   • Removed rows with missing values using .dropna() method and verified the removal.
   • Merged the cleaned dataset with the country code dataset using pd.merge().
3. Exploratory Data Analysis:
   • Online Delivery Analysis: Utilized seaborn's countplot() to analyze online delivery options.Extracted relevant data and visualized distribution.
   • Geographic Analysis: Employed matplotlib's pie() to analyze restaurant distribution.Extracted top city data and created pie chart.
   • Cuisine Analysis: Utilized matplotlib's pie() to analyze cuisine distribution. Extracted top cuisines data and created pie chart.

Key Findings

1. Rating Distribution: The majority of restaurants fall within the rating range of 2.5 to 3.4.
2. Online Delivery Availability: Online delivery options are prevalent in India and UAE, indicating potential market opportunities.
3. Customer Preferences: Indian customers contribute significantly to zero ratings, suggesting potential areas for improvement in service quality.
4. Popular Cuisines: Indian, Chinese, and Italian cuisines emerge as the top preferences among consumers, highlighting potential demand trends.

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Game DataSet Exploratory Data Analysis (EDA)

Introduction

This project focuses on conducting an in-depth Exploratory Data Analysis (EDA) on the Game dataset to extract valuable insights and trends.

• Main File: Game_EDA.ipynb

Analysis Objectives

1. Data Understanding: Gain a comprehensive understanding of the dataset's structure, contents, and attributes.
2. Data Cleaning: Implement data cleaning techniques to handle missing values, ensure data integrity, and prepare the dataset for analysis.
3. Exploratory Data Analysis (EDA): Utilize various statistical and visualization methods to explore relationships between features, identify patterns, and extract meaningful insights.
4. Insights Generation: Generate actionable insights and recommendations based on the findings from the EDA process.

Methodology

1. Data Preparation:
   • Imported necessary libraries including pandas, numpy, matplotlib, and seaborn to facilitate data manipulation and visualization.
   • Loaded the dataset using pd.read_csv() function with appropriate encoding to read the CSV files into pandas DataFrame.
   • Inspected the initial rows of the dataset using .head() method to understand its structure and content.
   • Reviewed the columns and their data types using .columns and .info() methods to identify potential issues.
   • Calculated basic statistical metrics for numerical features using .describe() method to gain insights into the data distribution.
2. Data Cleaning:
   • Identified missing values using .isnull().sum() and addressed them appropriately to ensure data completeness.
   • Visualized missing values using a heatmap with sns.heatmap() to identify patterns and understand the extent of missingness.
   • Removed rows with missing values using .dropna() method, considering the impact on the analysis and the dataset's integrity.
   • Standardized the format of certain columns (e.g., 'release_year', 'platform') to maintain consistency and facilitate analysis.
3. Exploratory Data Analysis:
   • Highest Rated Games Analysis: Utilized .groupby() and .sort_values() functions to identify games with the highest ratings for each year.
   • Most Popular Games Analysis: Employed .groupby(), .sort_values(), and .iloc[] methods to extract the most popular games based on user ratings and sales for each year.
   • Relationship Visualization: Created scatter plots using plt.scatter() to visualize the relationship between user ratings, sales, and platform, exploring potential correlations.
   • Genre Analysis: Investigated popular genres for each year using .groupby() and .idxmax() to identify the genre with the highest count.
   • Sales Revenue Analysis: Utilized .groupby() and .sort_values() to determine games with the highest and lowest sales revenue for each year.
   • Average Playtime Analysis: Calculated the average playtime for each year using .groupby() and .mean() functions.
   • Publisher Analysis: Examined the publisher with the highest number of games using .groupby() and .idxmax() to identify the most prolific publisher.
   • Rating Distribution Across Genre: Visualized the distribution of game ratings across different genres using .groupby() and sns.barplot() functions.
   • Top Rated Games Across Genre: Identified top-rated games across genres using .groupby(), .sort_values(), and .iloc[] methods.
   • Rating Trends Over Years: Plotted a line graph to analyze the change in game ratings over the years using .groupby() and sns.lineplot().
   • Rating Trends Across Genre Over Years: Created line graphs to explore how game ratings change over the years across different genres using .groupby() and sns.lineplot().
   • Top Developers Analysis: Determined the top developers with the most games released using .value_counts() and sns.barplot().
   • Sales Revenue Trends Analysis: Examined the relationship between years and sales revenue using boxplots with sns.boxplot().
   • Playtime vs. Average Revenue Analysis: Investigated the relationship between playtime and average revenue using line graphs with sns.lineplot().

Key Findings

1. Rating Distribution: The majority of games tend to have moderate ratings, with a few outliers at the extreme ends.
2. Genre Preferences: Certain genres maintain consistent popularity over the years, while others experience fluctuations, reflecting changing user preferences.
3. Sales Revenue Trends: Overall, there is a positive trend in sales revenue over the years, influenced by factors such as genre, platform, and marketing strategies.
4. Publisher Influence: Certain publishers consistently release successful games, contributing significantly to the gaming industry's success.
5. Playtime Impact: Game playtime varies widely across different genres and platforms, with some games offering longer playtime to accommodate complex storylines or multiplayer features, while others focus on shorter, more engaging experiences.

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