epmanu185/Alfido_tech_project

This Analysis involves 3 kaggle data sets ; Weather data, Uber data, Inventory data

Alfido_tech_project

Table of contents

1. Project overview
2. Data sources
3. Tools used
4. Data cleaning / preparation
5. Exploratory Data Analysis
6. Data analysis
7. Results / Findings
8. Recommendations
9. References

Project overview

This data analysis project aims to provide insights into weather data (2012), Uber data (2016) & Inventory data. By analyzing various aspects of the weather data, Uber data & Inventory seperately, we seek to identify trends, make data-driven recommendations, and gain a deeper understanding of these factors.

Data sources

The primary dataset used for this analysis are

• Weather data: kaggle weather
• Uber data: kaggle Uber
• Inventory data: kaggle inventory

Tools used

• EXCEL - For data cleaning. Download here
• SQL - For data analysing.
• Python - Exploratory data analysis.

Data cleaning / preparation

In the initial data preparation phase, we performed the following tasks:

1. Data loading and inspection.
2. Handling missing values.
3. Data cleaning and formatting.

Exploratory Data Analysis

EDA involves exploring the datas to answer key questions, such as:

1. Weather data

• What is the overall temperature trend?
• How often does it rains?
• What is the wind speed trend?

2. Uber data (2016)

• what is the most opted trip category and purpose?
• what are top 10 routes?
• which is the most opted route?
• which is the longest travelled route?
• which month has the most number of trips?

3. Inventory data

Data analysis

Include some interesting codes or features worked with

Weather data analysis

• Temperature trend analysis

SELECT DATE(Date_Time)AS date,ROUND(AVG(Temp_C ),0)AS temp_in_degree_celcius
FROM `weather_datas.weather`
GROUP BY date
ORDER BY date ASC;

• precipitation pattern analysis

SELECT weather,COUNT (Weather)AS weather_count
FROM `weather_datas.weather`
GROUP BY Weather
HAVING COUNT (Weather) >100
ORDER BY weather_count DESC;

• Windspeed analysis

SELECT DATE(Date_Time)AS date,ROUND (AVG (Wind_Speed_km_h),0)AS windspeed_km_hr, ROUND(AVG(temp_C),0)AS temp_in_degree_celcius
FROM `weather_datas.weather`
GROUP BY date
ORDER BY date ASC;

Uber data analysis

SELECT START_MONTH AS MONTH,ROUND (SUM(SUM_of_MILES),0)AS MILES_COVERED
FROM `braided-topic-402311.Babynames.uberr` 
GROUP BY START_MONTH
ORDER BY START_MONTH ASC;

SELECT month,SUM(count)AS count
FROM `braided-topic-402311.Babynames.ube`
GROUP BY month;

Inventory data analysis

Results / Findings

Weather Data analysis results are summarized as follows:

1. Coldest month is January and Hottest month is July.
2. 3.7% of the days in 2012 are rainy.
3. Average windspeed of 15 Km/hr with a maximum value of 42km/hr is observed.

Uber Data analysis results are summarized as follows:

1. The most opted trip category and purpose are Business and Meeting.
2. The most opted route is Morrisville - Cary.
3. The longest travelled route is Latta - Jacksonville.
4. December has the most number of trips.

References

1. kaggle.com