Update Case Study - Diamond Price Prediction.ipynb

Author: bansalkanav

Module 4 - Machine Learning/01. Data Preparation and Modelling with sklearn/2. Predicting Diamond Price/code/Case Study - Diamond Price Prediction.ipynb

@@ -7,13 +7,13 @@
  "source": [
  "# **Case Study - Diamond Price Prediction**\n",
  "\n",
- "### Business Understanding\n",
+ "### **Business Understanding**\n",
  "\n",
  "Diamond is one of the strongest and the most valuable substances produced naturally as a form of carbon. However, unlike gold and silver, determining the price of a diamond is very complex because many features are to be considered for determining its price.\n",
  "\n",
  "The value of diamonds depends upon their structure, cut, inclusions (impurity), carats, and many other features. The uses of diamonds are many, such as in industries, as they are effective in cutting, polishing, and drilling. Since diamonds are extremely valuable, they have been traded across different countries for centuries now and this trade only increases with time. They are graded and certified based on the \"four Cs\", which are color, cut, clarity, and carat. These are the only metrics that are being used to the quality of diamonds and sets the price of the diamond. This metric allows uniform understanding for people across the world to buy diamonds, which allows ease of trade and value for what is purchased.\n",
  "\n",
- "### Understanding the 4 C's\n",
+ "### **Understanding the 4 C's**\n",
  "- Cut\n",
  "- Color\n",
  "- Clarity\n",
@@ -27,14 +27,14 @@
  "Credits - https://www.brides.com/story/choosing-engagement-ring-four-cs\n",
  "\n",
  "\n",
- "### Problem Statement\n",
+ "### **Problem Statement**\n",
  "**Build a system which can take features of diamond like carat, cut, color, clarity, x, y, z, etc.. and predicts the `price` of diamond.**\n",
  "\n",
- "### Case Study Agenda\n",
+ "### **Case Study Agenda**\n",
  "\n",
  "In this notebook, you will learn:\n",
  "1. How to split the given data into Train and Test?\n",
- "2. How to perform Feature Engineering on Categorical and Numerical Features.\n",
+ "2. How to perform Feature Engineering on Categorical and Numerical Features?\n",
  " - Categorical Columns - OneHotEncoding and LabelEncoding\n",
  " - Numerical Columns - Standardization and Normalization\n",
  "3. How to build ML models that can predict Price of a Diamond?"
@@ -45,7 +45,7 @@
  "id": "7e428cca-faa9-4526-ae9a-6a0afd419bdd",
  "metadata": {},
  "source": [
- "## Import the required Libraries"
+ "## **Import the required Libraries**"
  ]
  },
  {
@@ -64,10 +64,10 @@
  },
  {
  "cell_type": "markdown",
- "id": "c3ad4012-041b-4050-ad69-688538d02cbf",
+ "id": "ec7f36ab-e492-4318-8e17-df87beb8ba79",
  "metadata": {},
  "source": [
- "## Load the Data"
+ "## **Step 1 - Load the Data**"
  ]
  },
  {
@@ -226,7 +226,7 @@
  "id": "ae107fc6-cc97-44dc-9b1a-db67fa6c6467",
  "metadata": {},
  "source": [
- "## Exploratory Data Analysis"
+ "## **Step 2 - Exploratory Data Analysis**"
  ]
  },
  {
@@ -534,7 +534,7 @@
  "id": "96acea47-a273-4ff2-8141-2cad2c34d9dc",
  "metadata": {},
  "source": [
- "## Data Preparation and Model Building Pipeline\n",
+ "## **Data Preparation and Model Building Pipeline**\n",
  "\n",
  "1. Identifying the inputs (X) and output (y)\n",
  "2. Split into train and test (X_train, X_test, y_train, y_test)\n",
@@ -575,7 +575,7 @@
  "id": "d8537fbc-a9a9-4bdf-a9bf-666f43196045",
  "metadata": {},
  "source": [
- "### Step 1 - Identify Predictors and Target Variables"
+ "## **Step 3 - Segregate Inputs (X) and Output (y)**"
  ]
  },
  {
@@ -594,7 +594,7 @@
  "id": "1fbbcff2-8bd4-4ded-8819-de2599f3736f",
  "metadata": {},
  "source": [
- "### Step 2 - Split the data into train and test"
+ "## **Step 4 - Split the data into Train and Test**"
  ]
  },
  {
@@ -629,7 +629,7 @@
  "id": "7bc55644-f263-4e74-9555-14454d3dfab8",
  "metadata": {},
  "source": [
- "### Step 3 - Data Preparation: Data Cleaning and Feature Engineering"
+ "## **Step 5 - Apply Data Preparation on Training Data**"
  ]
  },
  {
@@ -2053,9 +2053,9 @@
  "id": "5e64524f-ef70-4bc7-93d4-d5456b356535",
  "metadata": {},
  "source": [
- "### Step 5 - Preparing Test Data\n",
+ "## **Step 7 - Apply Data Preparation on Test Data**\n",
  "\n",
- "**Note that, Step-4 is discussed after this.**"
+ "**Note that, Step-6 is discussed after this.**"
  ]
  },
  {
@@ -2410,7 +2410,7 @@
  "id": "cfa1f6ed-3541-48f6-bdf0-88633ec20994",
  "metadata": {},
  "source": [
- "### Step 4, 6 and 7 - Training and Testing Phase - **Linear Regression**"
+ "## **Step 6, 8 and 9 - Training and Testing Phase (Linear Regression)**"
  ]
  },
  {
@@ -2613,7 +2613,7 @@
  "id": "8e44245a-2c03-4169-ac9f-4cc8ac7a1544",
  "metadata": {},
  "source": [
- "### Step 4, 6 and 7 - Training and Testing Phase - **KNN Regression**"
+ "## **Step 6, 8 and 9 - Training and Testing Phase (KNN Regression)**"
  ]
  },
  {
@@ -2818,7 +2818,7 @@
  "id": "84627ac9-d065-424c-9fe3-142af3d8c27f",
  "metadata": {},
  "source": [
- "### Step 4, 6 and 7 - Training and Testing Phase - **DT Regression**"
+ "## **Step 6, 8 and 9 - Training and Testing Phase (DT Regression)**"
  ]
  },
  {
@@ -3048,7 +3048,7 @@
  "id": "a6c657d3-c291-4a53-9358-c0583f8f12ef",
  "metadata": {},
  "source": [
- "### Step 4, 6 and 7 - Training and Testing Phase - **Random Forest Regression**"
+ "## **Step 6, 8 and 9 - Training and Testing Phase (Random Forest Regression)**"
  ]
  },
  {
@@ -3290,7 +3290,7 @@
  "id": "4c439801-7862-4c4e-a45a-d46e05bbee98",
  "metadata": {},
  "source": [
- "## Comparing all the Models"
+ "## **Comparing all the Models**"
  ]
  },
  {