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- # 📘 Movie Profitability Analysis - EDA Summary
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- ## 🎬 Dataset Overview
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  This project explores the **“Movies Metrics, Features and Statistics”** dataset from Kaggle.
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  The dataset contains **6,569 movies** and **32 features**, including:
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@@ -16,12 +16,12 @@ The goal is to understand which **pre-release factors** influence a movie’s ab
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  ---
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- ## 🎯 Prediction Question
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  **Can we predict whether a movie will be profitable using only pre-release features such as budget, runtime, genre, and production characteristics?**
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  ---
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- ## 🔍 Target Variable
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  Binary classification target:
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  - **is_profitable = 1** → if *Worldwide Gross > Production Budget*
@@ -29,13 +29,13 @@ Binary classification target:
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  ---
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- # 📊 Exploratory Data Analysis (EDA)
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  Below are the research questions and the insights based on the dataset’s visualizations.
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  ---
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- ## 1️⃣ How does production budget influence profitability?
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  ### **Visualization:** Profit vs. Production Budget
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  *[Profit X Budget](https://huggingface.co/datasets/Leelu1002/Movie_Profitability_Analysis/resolve/main/Profit%20X%20Budget.png)*
@@ -47,7 +47,7 @@ Below are the research questions and the insights based on the dataset’s visua
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  ---
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- ## 2️⃣ How does genre affect profitability?
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  ### **Visualization:** Profitability Rate by Genre
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  *[Profit X Genre](https://huggingface.co/datasets/Leelu1002/Movie_Profitability_Analysis/resolve/main/Profit%20X%20Genre.png)*
@@ -64,7 +64,7 @@ Less profitable genres include Drama and Documentary.
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  ---
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- ## 3️⃣ How does creative type impact profitability?
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  ### **Visualization:** Profitability Rate by Creative Type
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  *[Profit X Creative Type](https://huggingface.co/datasets/Leelu1002/Movie_Profitability_Analysis/resolve/main/Profit%20X%20Creative%20Type%20.png)*
@@ -81,7 +81,7 @@ Lower profitability: **Historical**, **Dramatization**, and **Unknown**.
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  ---
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- ## 4️⃣ How does running time correlate with profitability?
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  ### **Visualization:** Running Time Density (Profitable vs. Not)
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  *[Profit X Running Time](https://huggingface.co/datasets/Leelu1002/Movie_Profitability_Analysis/resolve/main/Profit%20X%20Running%20Time%20.png)*
@@ -93,7 +93,7 @@ Lower profitability: **Historical**, **Dramatization**, and **Unknown**.
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  ---
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- ## 5️⃣ How does production method influence profitability?
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  ### **Visualization:** Average Profit by Production Method
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  *[Profit X Production Method](https://huggingface.co/datasets/Leelu1002/Movie_Profitability_Analysis/resolve/main/Profit%20X%20Production%20Method.png)*
@@ -111,7 +111,7 @@ Lower profitability:
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  ---
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- # 🧠 Key Insights Summary
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  ### Strong Predictors of Profitability
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  - Production Budget
@@ -124,20 +124,20 @@ Lower profitability:
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  ---
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- # Final Summary
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  This analysis shows that **pre-release movie characteristics** can be used to meaningfully predict profitability.
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  The strongest indicators are **budget**, **genre**, **creative type**, and **production method**, while running time offers additional but weaker predictive value.
130
 
131
  ---
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- # 📂 Project Files
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  Below is a complete list of all files used throughout this project:
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- ## 📁 Dataset Files
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  - **movies_dataset.csv** — Original dataset downloaded from Kaggle
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  - **movies_cleaned.csv** — Cleaned version after handling missing values and removing duplicates
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- ## 📘 Notebook Files
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  - **Leelu_EDA_&_Dataset.ipynb** — Main notebook containing:
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  - Data loading
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  - Data cleaning
@@ -154,11 +154,11 @@ Below is a complete list of all files used throughout this project:
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  - **Profit X Creative Type.png** — Profitability by Creative Type
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  - **Profit X Production Method.png** — Average Profit by Production Method
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- ## 📝 Documentation
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  - **README.md** — Project summary and final results documentation
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  ---
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- # ✍️ Author
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  **Leelu Alfi**
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  Reichman University - Data Science Track
164
  2025
 
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+ # Movie Profitability Analysis - EDA Summary
2
 
3
+ ## Dataset Overview
4
  This project explores the **“Movies Metrics, Features and Statistics”** dataset from Kaggle.
5
  The dataset contains **6,569 movies** and **32 features**, including:
6
 
 
16
 
17
  ---
18
 
19
+ ## Prediction Question
20
  **Can we predict whether a movie will be profitable using only pre-release features such as budget, runtime, genre, and production characteristics?**
21
 
22
  ---
23
 
24
+ ## Target Variable
25
  Binary classification target:
26
 
27
  - **is_profitable = 1** → if *Worldwide Gross > Production Budget*
 
29
 
30
  ---
31
 
32
+ # Exploratory Data Analysis (EDA)
33
 
34
  Below are the research questions and the insights based on the dataset’s visualizations.
35
 
36
  ---
37
 
38
+ ## 1. How does production budget influence profitability?
39
 
40
  ### **Visualization:** Profit vs. Production Budget
41
  *[Profit X Budget](https://huggingface.co/datasets/Leelu1002/Movie_Profitability_Analysis/resolve/main/Profit%20X%20Budget.png)*
 
47
 
48
  ---
49
 
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+ ## 2. How does genre affect profitability?
51
 
52
  ### **Visualization:** Profitability Rate by Genre
53
  *[Profit X Genre](https://huggingface.co/datasets/Leelu1002/Movie_Profitability_Analysis/resolve/main/Profit%20X%20Genre.png)*
 
64
 
65
  ---
66
 
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+ ## 3. How does creative type impact profitability?
68
 
69
  ### **Visualization:** Profitability Rate by Creative Type
70
  *[Profit X Creative Type](https://huggingface.co/datasets/Leelu1002/Movie_Profitability_Analysis/resolve/main/Profit%20X%20Creative%20Type%20.png)*
 
81
 
82
  ---
83
 
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+ ## 4. How does running time correlate with profitability?
85
 
86
  ### **Visualization:** Running Time Density (Profitable vs. Not)
87
  *[Profit X Running Time](https://huggingface.co/datasets/Leelu1002/Movie_Profitability_Analysis/resolve/main/Profit%20X%20Running%20Time%20.png)*
 
93
 
94
  ---
95
 
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+ ## 5. How does production method influence profitability?
97
 
98
  ### **Visualization:** Average Profit by Production Method
99
  *[Profit X Production Method](https://huggingface.co/datasets/Leelu1002/Movie_Profitability_Analysis/resolve/main/Profit%20X%20Production%20Method.png)*
 
111
 
112
  ---
113
 
114
+ # Key Insights Summary
115
 
116
  ### Strong Predictors of Profitability
117
  - Production Budget
 
124
 
125
  ---
126
 
127
+ # Final Summary
128
  This analysis shows that **pre-release movie characteristics** can be used to meaningfully predict profitability.
129
  The strongest indicators are **budget**, **genre**, **creative type**, and **production method**, while running time offers additional but weaker predictive value.
130
 
131
  ---
132
+ # Project Files
133
 
134
  Below is a complete list of all files used throughout this project:
135
 
136
+ ## Dataset Files
137
  - **movies_dataset.csv** — Original dataset downloaded from Kaggle
138
  - **movies_cleaned.csv** — Cleaned version after handling missing values and removing duplicates
139
 
140
+ ## Notebook Files
141
  - **Leelu_EDA_&_Dataset.ipynb** — Main notebook containing:
142
  - Data loading
143
  - Data cleaning
 
154
  - **Profit X Creative Type.png** — Profitability by Creative Type
155
  - **Profit X Production Method.png** — Average Profit by Production Method
156
 
157
+ ## Documentation
158
  - **README.md** — Project summary and final results documentation
159
 
160
  ---
161
+ # Author
162
  **Leelu Alfi**
163
  Reichman University - Data Science Track
164
  2025