Ghulam Murtaza Portfolio

Expert in SQL analytics, delving deep into data to provide actionable insights and robust database solutions. Connect with me on @My_Linkedin.

Analyzing Environmental Insights Using Python and SQL

✅ Extracted insights from 53K environmental readings.

✅ Cleaned, preprocessed, and quality assured the dataset.

🌐 SQL Queries:

1️⃣ Average temperature recorded for each device.

2️⃣ Top 5 devices with highest average carbon monoxide levels.

3️⃣ Average temperature recorded in the dataset.



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Covid-19 Data Analysis Using SQL and Data Visualization Using Tableau

🔧 Tools: Python, MySQL

📊 Data: COVID19 DATA from https://ourworldindata.org/covid-deaths

🌐 SQL Queries

📊 Queries for total cases, deaths, death percentage by location and date.

💀 Likelihood of Dying: Calculate "DeathPercentage" in different countries.

📈 Percent Population Infected: Analyze the percentage of population infected ("PercentPopulationInfected1").

🌍 Countries with Highest Infection Rates and Death Counts.



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PlayStore Apps Data Analysis Using Python and SQL

🛠️ Tools: Python, SQL, Pandas

📊 Dataset: Playstore apps dataset (26,000 records)

✅ Cleaned and transformed a dataset of 26,000 mobile app records, removing unnecessary columns and improving efficiency.

📈 SQL Queries:

1️⃣ Identified top 10 apps with the highest ratings.

2️⃣ Obtained installs and reviews for the top 5 most reviewed apps.

3️⃣ Found the app with the highest number of reviews and its category.



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Youtube Songs and Spotify Streams Analysis Using Python and SQL

🛠️ Tools: Python, SQL, Pandas

✅ Cleaned and transformed a massive dataset of twenty thousand users, reducing data size by 30% through removal of unnecessary columns, handling null values, deduplication, and conversion of durations to minutes.

🌐 SQL Queries:

1️⃣ Most viewed YouTube track.

2️⃣ Most streamed Spotify track.



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SQL Data Cleaning

📅 Standardized the Sale Date Column: Transformed the `sale_date` column to DATE format, removing the time component.

🔍 Addressed Null Values: Filled null entries in the property address column using Parcel ID-based self joins.

📍 Optimized Address Data: Segregated the property address into distinct address and city columns.

✉ Further Address Optimization: Split the owner address into address, city, and state columns for detailed data analysis.

🧹 Data Cleaning Initiatives: Purged duplicate rows, deleted superfluous columns, and standardized data formats for precise interpretation.

💡 Actionable Insights: Recommended cleaning further columns, visual data exploration, predictive housing price models, and enriching the dataset with external data sources.

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