An Exploratory Data Analysis of Indian Start-up Funding from 2018 to 2021.
Published: 2023-01-16 18:44:06

Exploratory Data Analysis (EDA) is a technique used to examine and summarize the main characteristics of a dataset, through visual and numerical methods.

## 1.01 Introduction
Exploratory Data Analysis (EDA) is a crucial step in understanding and analyzing data, particularly when it comes to Indian startups from the year 2018 to 2021. In this article, we will take a look at how I applied some key aspects of EDA to my project (analyzing Indian startups from the periods 2018 to 2021).

First, it is important to understand the data that we will be working with. Indian startups have seen a significant increase in funding and investments over the past few years, with the number of startups in the country growing rapidly. From 2018 to 2021, the number of Indian startups have grown by over 20%, with many of these startups focused on technology and e-commerce.

When conducting EDA, it is important to first understand the project and what your clients needs. In this project, my clients are potential investors interested in startups in the Indian ecosystem. This includes analyzing the types of variables present in the data, such as categorical or numerical, as well as the distribution of these variables. Additionally, it is important to identify any missing or outliers in the data since these can significantly impact the results of my analysis.

I used six stages of data analysis (as learnt from Google Data Analytics program) which are: Ask, Prepare, Process, Analyze, Share, and Act. _source: https://github.com/sj50179/Google-Data-Analytics-Professional-Certificate/wiki/1.3.2.Outlining-the-data-analysis-process_

## 1.1 Understanding the Data
The data was in a separate CSV file, meaning they had to be put together at a point. The shared columns in the files were as follows (column name: description):
Company/Brand: name of the start-up
Founded: the year the start-up was founded
Sector: sector of operation
What it does: description of the company
Founders: founders of the company
Investor: investors in the deal
Amount($): raised funds
Stage: round of funding
Headquarters: headquarters of the company

## 2.0 Ask Stage
In this phase, I did two things. I defined the problem to be solved and made sure that I fully understood the team's expectations since they hold a stake in the project.
Here, with the ultimate aim of making a recommendation to the team to assist their goal of entering the Indian ecosystem, the following hypothesis was stated and questions were asked to guide the analyses of the project.

## 2.1 Hypothesis

- Null hypothesis  - Low risk start-ups tend to attract higher investors
- Alternate hypothesis  - High risk start-ups tend to deflect investors interest or attract lower funding.

## 2.2 Questions
For the hypothesis, the following questions were put forward to be answered:

- How does the age of a company impact investment opportunity?
- Which investment level stage attract more investors?
- What sector will a start-up attract more investors or funding?
- Will a location of a start-up affect investment drive?
- What economic activity of a start-up boost investor funding?

## 3.0 Data Preparation and Processing
The goal of data preparation and processing is to ensure that the data is in a format that can be easily and accurately analyzed or modeled, and that any errors or inconsistencies in the data are identified and addressed.

## 3.1 Loading packages
Basic packages for the analysis were loaded. These were:

- Pandas: for data cleaning and manipulation
- Numpy: for data cleaning and manipulation
- Matplotlib: for visualizations
- Seaborn: for visualizations
- Re: for regular expressions
- Warnings: to deal with the warnings

## 3.2 Notes from Previewing the DataFrames
The individual datasets were then loaded as Pandas DataFrames. The first note was the differences in the number of columns in the datasets: 2018 (6), 2019 (9), 2020 (10), and 2021 (9). Other observations from looking at the individual datasets are as follows

## 3.2.1 The 2018 DataFrame

- The DataFrame has 526 rows and 6 columns.
- Dashes were used in the amounts column for deals whose values were not known.
- The amounts in the 2018 DataFrame are a mix of Indian Rupees (INR) and US Dollars (USD), meaning they have to be converted into the same currency.
- The industry and location columns have multiple information. A decision is to be made between selecting the first value before the separator(,) as the main value or representing that column with a word cloud.

## 3.2.2 The 2019 DataFrame

- The DataFrame has 89 rows and 9 columns.
- The data type of the "Founded" column is set to float64. It should be set to a string for uniformity.
- The headquarter column has multiple information. A decision is to be made between selecting the first value before the separator(,) as the main value or representing that column with a word cloud.

## 3.2.3 The 2020 DataFrame
There is an extra column called "Unnamed:9", giving it a total of 10 columns. It should be dropped to ensure complete alignment with the other DataFrames for ease of concatenation.

## 3.2.4 The 2021 DataFrame
The data type of the "Founded" column is set to float64. It should be set to a string for uniformity.

## 3.2.5 General Notes

- The columns in 2018 are different from those of 2019–2021, meaning they have to be renamed before concatenation.
- The currency signs and commas have to be removed from each amount column for each DataFrame.
- All the columns with amounts have to be set to float.
- All the years of funding and the years founded should be converted to strings.
- The respective years of funding have to be attached to each DataFrame before combining.

## 3.3 Assumptions

- The average Indian Rupee (INR) to US Dollar (USD) rate for the relevant year will be used for currency conversions.
- Amounts without currency symbols in the 2018 dataset are in USD.

## 3.4 Cleaning the Data

- I applied string formatting to all columns except the amounts columns which were formatted as numeric.
- I separated the values in the location and industry columns using a comma as the delimiter and selected the first value in the split column as the primary sector.
- For the 2018 Amount column, I created two new columns to help with the currency conversion and then dropped them after standardizing the amounts.
- I removed all commas and currency signs from the "Amounts" columns.
- I replaced the "Undisclosed" text in the "Amounts".
- I replaced the "nan" values in the "Founders" column with nulls.
- I replaced notable misplaced and/or erroneous values in the respective rows.
- I dropped the extra unnamed in the 2020 DataFrame.
- For each DataFrame, I appended a column indicating the year of funding

- The columns in the 2018 DataFrame were renamed to match the other DataFrames, after which it was concatenated with the other DataFrames.

- I then brushed up with the following:
Reformatted all amounts as numeric and replaced nulls with zeros.
Formatted funding years and years founded as strings.
Dropped all duplicates and reset the index.
Did more column-specific cleaning (ref. notebook).

## 4.0 Answering the Questions
Here, I combine the "Analyze" and "Share" stages of the data analysis process through the code and visualizations.

#### _4.1 1. How does the age of a company impact investment opportunity?_
From the trend below, age of the company in relation to funding in a given year has no influence on investment choices. The discrepancies between the years, particularly between the data from 2011 and 2015, indicate that economic variables such as the consumer price index and interest rates may be the real drivers of the amount of capital provided, not the company's age.

![Image description](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/l26iqjhl5vj9237p1sj6.png)

#### _4.2 Which investment level stage attract more investors?_
From the analysis, Venture-Series Unknown is in the majority for funding for startups, but this is not relevant since the series of funding is unknown. Hence, it can be argued that investors provide additional money to a start-up during its seed stage, then again during its Series A stage. This demonstrates that the majority of investors in the Indian startup ecosystem favor early investments.

![Image description](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/jetn9wdj7crqssaf8qv1.png)

#### _4.3 What sector will a start-up attract more investors or funding?_
Fintechs, Edtech and Tech are the sectors that receive the most funding according to the two graphs displayed with Fintech leading the way. These industries attract the majority of startups and receive more funding than other industries.


![Image description](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/h51b6v56bl8dtpa0gvqr.png)

#### _4.4 Will a location of a start-up affect investment drive?_
Bangalore is known as the Silicon Valley of India since it has the greatest start-up and investment. According to the bar graph, the majority of startups are located in the top 4 spots and receive more funding.


![Image description](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/hebazczkz9xrczmx51wo.png)
_

#### 4.5 What economic activity of a start-up boost investor funding?
_

![Image description](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/m3o4swbsu5l96fawi2bt.png)
Major activities that boost investor funding are venture debt financing, online shopping and chain of neighbourhood stores and supermarkets(e-commerce and commerce). Among these are companies also run a cloud data platform providing data warehousing services, energy, petrochemicals, textiles, and also educational technology that develops personalized learning programs.

## 5.0 Conclusion and Recommendations

The ACT stage of data analysis stands for "Actions and Conclusion, Tracking." It is the final stage in the data analysis process, where the insights and findings from the previous stages are used to make decisions and take action. The goal of this stage is to turn the data into useful information that can drive business decisions and improve performance.

## 5.1 Recommendations to the Team

1. The majority of start-ups in India revolve around technology, with the majority of them being FINTECHS.
2. In India, finance and location are positively associated. If we locate our startup in Bangalore, there is a strong likelihood that we will find finance quickly.
3. We should anticipate more funding in a given year the more start-ups there are.
4. Investors favor the seed stage of a business.

## 6.0 Final Notes

Thank you so much for reading.
I will be grateful for your comments, advice, suggestions, and recommendations. Too long or too short? Too detailed or missing some details? Please let me know. You can leave a comment here or find me on Dev @violetteallotey 
I'm also open to collaborating on projects.