Update README.md
Browse files
README.md
CHANGED
|
@@ -5,26 +5,10 @@ In this project, I analyzed NBA Draft data from 1989 to 2021.
|
|
| 5 |
The main goal was to understand what makes a player a **Top 10 draft pick**,
|
| 6 |
and whether those players are really better than the rest.
|
| 7 |
|
| 8 |
-
---
|
| 9 |
-
|
| 10 |
-
## Step 1 – Data Cleaning
|
| 11 |
-
Before starting the analysis, I cleaned the data to make it accurate and consistent:
|
| 12 |
-
- Removed text columns that were not relevant (player name, team, college)
|
| 13 |
-
- Checked for missing values and filled them with the mean
|
| 14 |
-
- Removed duplicate rows
|
| 15 |
-
|
| 16 |
-
After cleaning, the dataset was ready for analysis.
|
| 17 |
-
|
| 18 |
-
---
|
| 19 |
-
|
| 20 |
-
## Step 2 – Outlier Detection
|
| 21 |
-
Some players had extreme values (for example, superstars with very high stats).
|
| 22 |
-
I used the **IQR (Interquartile Range)** method to remove those outliers,
|
| 23 |
-
so that the results would be more balanced and realistic.
|
| 24 |
|
| 25 |
---
|
| 26 |
|
| 27 |
-
##
|
| 28 |
I examined several numeric features such as:
|
| 29 |
- Points per game
|
| 30 |
- Total games played
|
|
@@ -35,7 +19,7 @@ players who score more points usually also have higher win share values.
|
|
| 35 |
|
| 36 |
---
|
| 37 |
|
| 38 |
-
##
|
| 39 |
|
| 40 |
### Question 1:
|
| 41 |
Do Top 10 players score more points per game?
|
|
@@ -77,25 +61,6 @@ showing that they contribute more to their teams' success.
|
|
| 77 |
|
| 78 |
---
|
| 79 |
|
| 80 |
-
## Tools Used
|
| 81 |
-
- Python (Pandas, NumPy)
|
| 82 |
-
- Matplotlib, Seaborn
|
| 83 |
-
- Google Colab
|
| 84 |
-
- Hugging Face Datasets
|
| 85 |
-
|
| 86 |
-
---
|
| 87 |
-
|
| 88 |
-
## Files in This Project
|
| 89 |
-
- `nbaplayersdraft.csv` – the dataset file
|
| 90 |
-
- `nba_draft_EDA_EDA_&_Dataset.ipynb` – Jupyter Notebook with the code and analysis
|
| 91 |
-
- `README.md` – this file
|
| 92 |
-
- Graphs:
|
| 93 |
-
- `boxplot_points_per_game.png`
|
| 94 |
-
- `boxplot_games_played.png`
|
| 95 |
-
- `boxplot_win_shares.png`
|
| 96 |
-
|
| 97 |
-
---
|
| 98 |
-
|
| 99 |
## Video Presentation
|
| 100 |
A short 2–3 minute video summarizing my process and results.
|
| 101 |
*(I’ll add the video link here after uploading it.)*
|
|
|
|
| 5 |
The main goal was to understand what makes a player a **Top 10 draft pick**,
|
| 6 |
and whether those players are really better than the rest.
|
| 7 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 8 |
|
| 9 |
---
|
| 10 |
|
| 11 |
+
## Descriptive Statistics
|
| 12 |
I examined several numeric features such as:
|
| 13 |
- Points per game
|
| 14 |
- Total games played
|
|
|
|
| 19 |
|
| 20 |
---
|
| 21 |
|
| 22 |
+
## Research Questions and Visualizations
|
| 23 |
|
| 24 |
### Question 1:
|
| 25 |
Do Top 10 players score more points per game?
|
|
|
|
| 61 |
|
| 62 |
---
|
| 63 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 64 |
## Video Presentation
|
| 65 |
A short 2–3 minute video summarizing my process and results.
|
| 66 |
*(I’ll add the video link here after uploading it.)*
|