-
Notifications
You must be signed in to change notification settings - Fork 0
/
Random_Forest.html
132 lines (116 loc) · 8.38 KB
/
Random_Forest.html
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1">
<link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/@picocss/pico@1/css/pico.min.css">
<style>
body {
font-size: 0.7rem; /* Base font size */
}
.container {
max-width: 700px; /* Max-width for the container */
margin: auto; /* Center the container */
}
figure img {
width: 100%; /* Full width of its container */
max-width: 450px; /* Max width of the image */
height: auto; /* Maintain aspect ratio */
display: block; /* Block display to respect max-width */
margin: 0 auto; /* Center the image */
}
h2 {
font-size: 1.2rem; /* Heading size */
}
h3 {
font-size: 1rem; /* Subheading size */
}
p {
font-size: 0.7rem; /* Paragraph text size */
}
/* Ensure the main content is visible */
main {
padding: 1rem 0; /* Add padding around main content */
}
nav ul {
padding: 0.5rem 0; /* Padding for nav */
list-style-type: none; /* Remove list bullets */
margin: 0; /* Remove default margin */
padding: 0; /* Remove default padding */
display: flex; /* Add this line */
flex-wrap: nowrap; /* Add this line to prevent wrapping */
}
nav ul li {
display: inline; /* Display nav items in a line */
margin-right: 10px; /* Right margin for spacing */
}
nav ul li a {
color: #007bff; /* Set link color */
text-decoration: none; /* Remove text underline */
font-size: 0.6rem; /* Smaller font size for nav links */
padding: 5px 10px; /* Add padding to give a button-like appearance */
border-radius: 50px; /* Rounded corners */
transition: background-color 0.3s, color 0.3s; /* Transition for smooth color change */
background-color: #007bff; /* Set background color */
color: #fff; /* Set text color */
}
/* Change color on hover */
nav ul li a:hover {
background-color: #0056b3; /* Change background color on hover */
color: #fff; /* Keep text color the same on hover */
text-decoration: underline; /* Add underline on hover */
}
</style>
<title>Titanic Survival Project</title>
</head>
<body>
<nav class="container-fluid">
<ul>
<li><strong style="border: 2px solid #007bff; padding: 5px; border-radius: 10px;">Random Forest Models</strong></li> </ul>
<ul>
<li><a href="index.html" target="_blank">Home</a></li>
<li><a href="https://github.com/23yem/Final-Titanic-Machine-Learning" target="_blank">GitHub Repository</a></li>
<li><a href="Neural_Network.html" target="_blank">Neural Networks</a></li>
<li><a href="Gradient_Boosted.html" target="_blank">Gradient Boosted Trees</a></li>
<li><a href="Random_Forest.html" target="_blank">Random Forest</a></li>
</ul>
</nav>
<main class="container">
<div class="grid">
<section>
<hgroup>
<h2>Random Forest Model</h2>
<h3>From the TensorFlow Decision Forests (TFDF)</h3>
</hgroup>
<p>This page is used to describe my <a href="https://github.com/23yem/Final-Titanic-Machine-Learning/blob/main/CLEAN_titanic-RandomForest.ipynb" target="_blank">CLEAN_titanic-RandomForest.ipynb</a> notebook. Look below for a summary of my notebook and please feel free to look over my notebook</p> <figure>
<img src="https://serokell.io/files/vz/vz1f8191.Ensemble-of-decision-trees.png" alt="Random Forest" style="width: 500px; height: 300px" />
</figure>
</section>
</div>
<h1>Comprehensive Analysis of the Random Forest Model</h1>
<h2>1. Beginning the Journey: Environment and Tool Setup</h2>
<p>The journey into data analysis begins with setting up a powerful Python environment, equipped with libraries such as NumPy and Pandas. This foundational step is crucial, as it provides the necessary tools for effective data manipulation and analysis. The Kaggle Python Docker image defines this environment, offering a comprehensive suite of analytics tools ready to unravel the complexities of the dataset.</p>
<h2>2. Data Loading: The Gateway to Analysis</h2>
<p>Our first significant step is loading the Titanic dataset. This crucial phase involves importing the data into our Python environment, setting the stage for our analysis. Each row and column in this dataset is not just a number but a story, waiting to be deciphered and understood. This step is the beginning of our deep dive into the historical data, ready to reveal its hidden narratives.</p>
<h2>3. Data Preprocessing: Refining the Raw Data</h2>
<p>Data preprocessing is an essential step where we cleanse and prepare our dataset for analysis. This process involves handling missing values, encoding categorical variables, and normalizing data to ensure consistency and reliability. It’s like polishing raw diamonds, turning them into valuable insights that can be utilized effectively in our model.</p>
<h2>4. Feature Engineering: Extracting the Essence</h2>
<p>In feature engineering, we delve deeper into the dataset to identify and extract relevant features that significantly impact our model's predictive performance. This step involves analyzing various attributes, selecting those that provide the most value, and transforming them into a format suitable for machine learning models. It's akin to mapping the most promising paths in a vast terrain of data.</p>
<h2>5. Model Building with Random Forest: Constructing the Prediction Framework</h2>
<p>The construction of the Random Forest model marks a pivotal phase in our analysis. This sophisticated machine learning technique is employed to analyze the data and make predictions. The Random Forest algorithm, known for its accuracy and ability to handle complex datasets, provides a robust framework for our predictive analysis.</p>
<h2>6. Exploring Libraries: TensorFlow Decision Forests and Scikit-learn</h2>
<p>In our quest for the optimal model, we explore various libraries, including TensorFlow Decision Forests (TFDF) and Scikit-learn. These powerful tools offer different approaches and algorithms for building machine learning models. Their utilization exemplifies the versatility of our analysis, allowing us to compare and select the most effective methods for our dataset.</p>
<h2>7. Hyperparameter Tuning: Fine-Tuning the Model</h2>
<p>Hyperparameter tuning is an intricate process where we adjust the settings of our Random Forest model to enhance its performance. This critical step involves experimenting with various parameters to find the most effective combination for the model. It’s like tuning a musical instrument to produce the perfect harmony of predictions.</p>
<h2>8. Model Training: Teaching the Model to Learn</h2>
<p>Training the model is where we teach it to understand and learn from our dataset. This stage is crucial as the model iteratively adjusts its parameters to better fit the data. It’s akin to a learning journey, where the model gradually becomes more adept at making accurate predictions.</p>
<h2>9. Model Evaluation: Assessing Predictive Accuracy</h2>
<p>Model evaluation is where we assess the effectiveness and accuracy of our Random Forest model. This involves using various metrics to gauge its performance on test data. It’s a critical stage for verifying the model’s ability to predict outcomes accurately, ensuring the reliability of our analysis.</p>
<h2>10. Gleaning Insights: Concluding the Analysis</h2>
<p>The final step in our journey is drawing insights and conclusions from the model’s performance. This phase involves interpreting the results, understanding the model's strengths and limitations, and gleaning actionable insights. It's where we synthesize our findings, contributing to a deeper understanding of the Titanic dataset through the lens of our Random Forest analysis.</p>
</main>
<footer class="container">
<small><a href="index.html">Home</a> </small>
</footer>
</body>
</html>