Phase 2 project hellen

student.ipynb

@@ -7,13 +7,109 @@
     "## Final Project Submission\n",
     "\n",
     "Please fill out:\n",
-    "* Student name: \n",
-    "* Student pace: self paced / part time / full time\n",
+    "* Student name: Calvine Dasilver\n",
+    "* Student pace: full time\n",
     "* Scheduled project review date/time: \n",
-    "* Instructor name: \n",
+    "* Instructor name: Nikita\n",
     "* Blog post URL:\n"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " #  Demystifying House Sales Analysis with Regression Modeling in a Northwestern County\n",
+    "\n",
+    "   ## Project Overview"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "   ### <li>**Business Understanding**\n",
+    "\n",
+    "The real estate market plays a crucial role in the economic health and stability of a region. Understanding the factors that influence house prices is essential for both buyers and sellers to navigate the market effectively. This project focuses on a specific northwestern county in the United States, aiming to shed light on the key determinants of property valuation in this area.\n",
+    "   ##### Problem Statements:\n",
+    "What are the most significant factors influencing house prices in this northwestern county?How can we quantify the relationship between these factors and property value?Can we develop a reliable model to predict house prices based on relevant characteristics?\n",
+    "   ##### Challenges:\n",
+    "* Real estate data can be complex and multifaceted, encompassing various property features and local market trends.\n",
+    "* Accurately identifying and quantifying the relative impact of each factor on house prices can be challenging.\n",
+    "* External factors like economic conditions and interest rates might also influence prices, requiring careful consideration.\n",
+    "\n",
+    "##### Proposed Solutions:\n",
+    "We propose utilizing multiple linear regression, a powerful machine learning technique. This method allows us to analyze a large dataset of house sales and identify the statistical relationships between various property features (e.g., square footage, number of bedrooms, location) and the corresponding sale prices.\n",
+    "   ##### Objectives:\n",
+    "1. Develop a robust multiple linear regression model that accurately predicts house prices in the chosen northwestern county.\n",
+    "2. Identify the most significant factors influencing property value within this specific market.\n",
+    "3. Provide valuable insights into the housing market dynamics of the region, benefiting potential buyers, real estate agents, and other stakeholders.\n",
+    "   \n",
+    " \n",
+    "**Research questions that would help to achieve the objectives**:\n",
+    "\n",
+    "1. How does the number of bedrooms, bathrooms, grade and square footage of a house correlate with its sale price in King County?\n",
+    "2. How much can a homeowner expect the value of their home to increase after a specific renovation project?\n",
+    "3. Which renovation projects have the most significant impact on a home's market value in the northwestern county?\n",
+    "4. Are there specific combinations of renovation projects that provide an interdependent effect on a home's market value?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "   ### <li> **Data Understanding**\n",
+    "\n",
+    "Our analysis leverages the King County House Sales dataset - a rich resource containing over 21,500 records and 20 distinct features(columns). Spanning house sales from May 2014 to May 2015, this dataset provides a comprehensive snapshot of the King County housing market during that period.\n",
+    "\n",
+    "**The King County House Sales dataset contains the following columns;**\n",
+    "\n",
+    "id - unique identified for a house\n",
+    "\n",
+    "date - Date house was sold \n",
+    "\n",
+    "Price - Sale price (prediction target)\n",
+    "\n",
+    "bedrooms - Number of bedrooms,\n",
+    "\n",
+    "bathrooms - Number of bathrooms,\n",
+    "\n",
+    "sqft_living - Square footage of living space in the home,\n",
+    "\n",
+    "sqft_lot - Square footage of the lot,\n",
+    "\n",
+    "floors - Number of floors (levels) in house,\n",
+    "\n",
+    "view - Quality of view from house,\n",
+    "\n",
+    "condition - How good the overall condition of the house is. Related to maintenance of house,\n",
+    "\n",
+    "grade - Overall grade of the house. Related to the construction and design of the house,\n",
+    "\n",
+    "sqft_above - Square footage of house apart from basement,\n",
+    "\n",
+    "sqft_basement - Square footage of the basement,\n",
+    "\n",
+    "yr_built - Year when house was built,\n",
+    "\n",
+    "yr_renovated - Year when house was renovated,\n",
+    "\n",
+    "zipcode - ZIP Code used by the United States Postal Service,\n",
+    "\n",
+    "sqft_living15 - The square footage of interior housing living space for the nearest 15 neighbors,\n",
+    "\n",
+    "sqft_lot15 - The square footage of the land lots of the nearest 15 neighbors, and\n",
+    "\n",
+    "sell_yr - Date house was sold.\n",
+    "\n",
+    "\n",
+    "We need to be aware of certain constraints within the data, as these might influence our analysis and interpretation of the results. From the sources;\n",
+    "\n",
+    "1. The data may contain anomalies or inconsistencies that require careful examination during analysis. For instance, a record lists a house with 33 bedrooms, which appears to be an outlier\n",
+    "\n",
+    "2. It's important to consider the time frame of the data (May 2014 - May 2015) as it may not fully capture the current market dynamics in King County.\n",
+    "3. It's important to acknowledge the scope of the data. While it provides details on house features, it may not capture external factors such as interest rates or the overall economic climate, which can also play a role in determining property values."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -26,21 +122,22 @@
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+   "display_name": "Python (learn-env)",
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