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Coffee Sales Dashboard with Power BI: Daily Trends, Top Flavors, and Peak Hours Analysis

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In the competitive coffee business, understanding sales trends is essential for improving marketing strategies, optimizing inventory, and maximizing profits. In this project, I built a Coffee Sales Dashboard in Power BI to help a coffee shop analyze its performance through interactive visuals and actionable insights. The dashboard answers key business questions: 📅 How do sales change by day ? 📈 What is the average daily sales for the year ? ☕ Which are the most popular coffee flavors ? 🕐 What is the busiest time of the day for sales? The dataset contains the following columns: Date – Date of the transaction Time – Time of the sale Product – Coffee flavor or type Quantity – Units sold Sales – Total revenue per transaction I imported the data into Power BI and performed initial cleaning using Power Query , including: Removing duplicates and nulls Converting Date and Time to proper formats Extracting Day , Month , and Hour from date/t...
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Liner Aggression Classification

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The Titanic dataset is one of the most famous beginner-friendly datasets in data science. It contains information about passengers aboard the Titanic — including age, gender, class, and survival status. In this project, I built a Logistic Regression model to predict whether a passenger survived based on their characteristics. Through data cleaning, visualization, feature engineering, and model building, we can uncover the key factors that influenced survival. . Dataset & Tools Dataset: Titanic Dataset (Kaggle) Tools: Python, Pandas, NumPy, Seaborn, Matplotlib, Scikit-learn Skills Demonstrated: Data Cleaning & Preprocessing Exploratory Data Analysis (EDA) Feature Engineering Logistic Regression Modeling Model Evaluation 2. Load and Explore the Dataset import numpy as np import pandas as pd import matplotlib.pyplot as plt import seaborn as sns # Check for missing data titanic.isnull () . sum () PassengerId 0 Survived 0 Pclass ...
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Data Analytics On Tips Dataset  In this project, I analyzed the popular “Tips” dataset from the Seaborn library. This dataset contains information about restaurant bills, tips, and customer details. The goal was to: Explore and visualize tipping patterns. Discover factors that influence tip amounts. Build a simple predictive model to estimate tips based on bill size and other factors 1. Dataset & Tools Dataset:       tips (Seaborn built-in dataset)       Tools Used: Python, Pandas, Seaborn, Matplotlib, Scikit-learn       Skills Demonstrated: Data Cleaning, EDA, Data Visualization, Linear Regression 2. Load and Explore the Dataset tips.head () total_bill tip sex smoker day time size 0 16.99 1.01 Female No Sun Dinner 2 1 10.34 1.66 Male No Sun Dinner 3 2 21.01 3.50 Male No Sun Dinner 3 3 23.68 3.31 Male No Sun Dinner 2 4 24.59 3.61 Female No Sun Dinner 4 3.Apply Lamda Function tips [ 'total_bill' ] .apply ...
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Data Analysis On Titanic Dataset

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In this project, I explored the classic Titanic dataset to understand passenger survival patterns using data visualization and built a machine learning model to predict survival chances. This project demonstrates key data analysis skills, including data cleaning, visualization, feature engineering, and predictive modeling. Dataset & Tools Dataset: Titanic Dataset (Kaggle) Tools: Python, Pandas, Matplotlib, Seaborn, Scikit-learn Skills Demonstrated: Data Cleaning & Exploration, Visualization & Insight Discovery,                           Predictive Modeling (Logistic Regression) A Small Example for Graphs and Importing Pandas import pandas as pd # Time Series ;  Time Stamped data; # ex. weather data (temp), stock prices; gold price,    -> line plot We start by importing and cleaning the dataset:  titanic_df.head() A Simple plot to look at the data of peop...
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Data science blog

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  Data science is an interdisciplinary field that uses scientific methods, processes, algorithms and systems to extracting knowledge and insights from data in various forms, both structured and unstructured, similar to data mining.  The aim of data science is to make better decisions and predictions by using data. It is a process of making business decisions by using data. Data science is a combination of statistics, computer science and business. It is a process of making business decisions by using data. There are three main stages in data science: 1. Data collection 2. Data processing 3. Data analysis Data science is a multi-disciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from data in various forms, both structured and unstructured, similar to data mining. Data science is a concept to unify statistics, data analysis, machine learning and their related methods in order to understand and analyze actual phen...
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