diff --git a/student.ipynb b/student.ipynb
index d3bb34af..139e4f02 100644
--- a/student.ipynb
+++ b/student.ipynb
@@ -7,26 +7,457 @@
     "## 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",
+    "\n",
+    "   ##### Challenges of a Fluctuating Real Estate Market:\n",
+    "* Market fluctuations make it difficult for real estate agents to price houses and guide clients on offers.\n",
+    "* Rapid price fluctuations create a challenging environment for homebuyers, making it difficult to secure a good deal and avoid overpaying.\n",
+    "* Trying to pick the perfect moment to sell a house for maximum profit feels like playing the lottery – stressful, unpredictable, and with slim odds of success.\n",
+    "* High land prices and buyers struggling to afford homes make it difficult for builders to build new houses.\n",
+    "\n",
+    "   \n",
+    "##### Problem Statements:\n",
+    "We want to find out what makes houses expensive in a certain county in the northwest US. We'll also look at ways to measure how much these things like number of bedrooms or location affect the price. Finally, we'll see if we can build a tool to predict house prices based on these important features.\n",
+    "\n",
+    "   ##### Conclusion\n",
+    " Our study looked at how the ups and downs of the housing market in a northwestern county are making things tough for everyone involved. To help out, we're building a tool to predict house prices. This will give real estate agents valuable information so they can give their clients the best advice in this unpredictable market.\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",
+    "\n",
+    "\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",
+    "   "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "   ### <li> **Data Understanding**\n",
+    "\n",
+    "Our analysis leverages the King County House Sales dataset.This information is stored in a file called \"kc_house_data.csv\".\n",
+    " It's 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"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>id</th>\n",
+       "      <th>date</th>\n",
+       "      <th>price</th>\n",
+       "      <th>bedrooms</th>\n",
+       "      <th>bathrooms</th>\n",
+       "      <th>sqft_living</th>\n",
+       "      <th>sqft_lot</th>\n",
+       "      <th>floors</th>\n",
+       "      <th>waterfront</th>\n",
+       "      <th>view</th>\n",
+       "      <th>...</th>\n",
+       "      <th>grade</th>\n",
+       "      <th>sqft_above</th>\n",
+       "      <th>sqft_basement</th>\n",
+       "      <th>yr_built</th>\n",
+       "      <th>yr_renovated</th>\n",
+       "      <th>zipcode</th>\n",
+       "      <th>lat</th>\n",
+       "      <th>long</th>\n",
+       "      <th>sqft_living15</th>\n",
+       "      <th>sqft_lot15</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>7129300520</td>\n",
+       "      <td>10/13/2014</td>\n",
+       "      <td>221900.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>1180</td>\n",
+       "      <td>5650</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NONE</td>\n",
+       "      <td>...</td>\n",
+       "      <td>7 Average</td>\n",
+       "      <td>1180</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1955</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>98178</td>\n",
+       "      <td>47.5112</td>\n",
+       "      <td>-122.257</td>\n",
+       "      <td>1340</td>\n",
+       "      <td>5650</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>6414100192</td>\n",
+       "      <td>12/9/2014</td>\n",
+       "      <td>538000.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2.25</td>\n",
+       "      <td>2570</td>\n",
+       "      <td>7242</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>NO</td>\n",
+       "      <td>NONE</td>\n",
+       "      <td>...</td>\n",
+       "      <td>7 Average</td>\n",
+       "      <td>2170</td>\n",
+       "      <td>400.0</td>\n",
+       "      <td>1951</td>\n",
+       "      <td>1991.0</td>\n",
+       "      <td>98125</td>\n",
+       "      <td>47.7210</td>\n",
+       "      <td>-122.319</td>\n",
+       "      <td>1690</td>\n",
+       "      <td>7639</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>5631500400</td>\n",
+       "      <td>2/25/2015</td>\n",
+       "      <td>180000.0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>770</td>\n",
+       "      <td>10000</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>NO</td>\n",
+       "      <td>NONE</td>\n",
+       "      <td>...</td>\n",
+       "      <td>6 Low Average</td>\n",
+       "      <td>770</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1933</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>98028</td>\n",
+       "      <td>47.7379</td>\n",
+       "      <td>-122.233</td>\n",
+       "      <td>2720</td>\n",
+       "      <td>8062</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2487200875</td>\n",
+       "      <td>12/9/2014</td>\n",
+       "      <td>604000.0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>3.00</td>\n",
+       "      <td>1960</td>\n",
+       "      <td>5000</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>NO</td>\n",
+       "      <td>NONE</td>\n",
+       "      <td>...</td>\n",
+       "      <td>7 Average</td>\n",
+       "      <td>1050</td>\n",
+       "      <td>910.0</td>\n",
+       "      <td>1965</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>98136</td>\n",
+       "      <td>47.5208</td>\n",
+       "      <td>-122.393</td>\n",
+       "      <td>1360</td>\n",
+       "      <td>5000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1954400510</td>\n",
+       "      <td>2/18/2015</td>\n",
+       "      <td>510000.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2.00</td>\n",
+       "      <td>1680</td>\n",
+       "      <td>8080</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>NO</td>\n",
+       "      <td>NONE</td>\n",
+       "      <td>...</td>\n",
+       "      <td>8 Good</td>\n",
+       "      <td>1680</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1987</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>98074</td>\n",
+       "      <td>47.6168</td>\n",
+       "      <td>-122.045</td>\n",
+       "      <td>1800</td>\n",
+       "      <td>7503</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 21 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           id        date     price  bedrooms  bathrooms  sqft_living  \\\n",
+       "0  7129300520  10/13/2014  221900.0         3       1.00         1180   \n",
+       "1  6414100192   12/9/2014  538000.0         3       2.25         2570   \n",
+       "2  5631500400   2/25/2015  180000.0         2       1.00          770   \n",
+       "3  2487200875   12/9/2014  604000.0         4       3.00         1960   \n",
+       "4  1954400510   2/18/2015  510000.0         3       2.00         1680   \n",
+       "\n",
+       "   sqft_lot  floors waterfront  view  ...          grade sqft_above  \\\n",
+       "0      5650     1.0        NaN  NONE  ...      7 Average       1180   \n",
+       "1      7242     2.0         NO  NONE  ...      7 Average       2170   \n",
+       "2     10000     1.0         NO  NONE  ...  6 Low Average        770   \n",
+       "3      5000     1.0         NO  NONE  ...      7 Average       1050   \n",
+       "4      8080     1.0         NO  NONE  ...         8 Good       1680   \n",
+       "\n",
+       "   sqft_basement yr_built  yr_renovated  zipcode      lat     long  \\\n",
+       "0            0.0     1955           0.0    98178  47.5112 -122.257   \n",
+       "1          400.0     1951        1991.0    98125  47.7210 -122.319   \n",
+       "2            0.0     1933           NaN    98028  47.7379 -122.233   \n",
+       "3          910.0     1965           0.0    98136  47.5208 -122.393   \n",
+       "4            0.0     1987           0.0    98074  47.6168 -122.045   \n",
+       "\n",
+       "   sqft_living15  sqft_lot15  \n",
+       "0           1340        5650  \n",
+       "1           1690        7639  \n",
+       "2           2720        8062  \n",
+       "3           1360        5000  \n",
+       "4           1800        7503  \n",
+       "\n",
+       "[5 rows x 21 columns]"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "# Load the data\n",
+    "data = pd.read_csv('data/kc_house_data.csv')\n",
+    "data.head()"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 13,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Dataframe Info:\n",
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 21597 entries, 0 to 21596\n",
+      "Data columns (total 21 columns):\n",
+      " #   Column         Non-Null Count  Dtype  \n",
+      "---  ------         --------------  -----  \n",
+      " 0   id             21597 non-null  int64  \n",
+      " 1   date           21597 non-null  object \n",
+      " 2   price          21597 non-null  float64\n",
+      " 3   bedrooms       21597 non-null  int64  \n",
+      " 4   bathrooms      21597 non-null  float64\n",
+      " 5   sqft_living    21597 non-null  int64  \n",
+      " 6   sqft_lot       21597 non-null  int64  \n",
+      " 7   floors         21597 non-null  float64\n",
+      " 8   waterfront     19221 non-null  object \n",
+      " 9   view           21534 non-null  object \n",
+      " 10  condition      21597 non-null  object \n",
+      " 11  grade          21597 non-null  object \n",
+      " 12  sqft_above     21597 non-null  int64  \n",
+      " 13  sqft_basement  21597 non-null  object \n",
+      " 14  yr_built       21597 non-null  int64  \n",
+      " 15  yr_renovated   17755 non-null  float64\n",
+      " 16  zipcode        21597 non-null  int64  \n",
+      " 17  lat            21597 non-null  float64\n",
+      " 18  long           21597 non-null  float64\n",
+      " 19  sqft_living15  21597 non-null  int64  \n",
+      " 20  sqft_lot15     21597 non-null  int64  \n",
+      "dtypes: float64(6), int64(9), object(6)\n",
+      "memory usage: 3.5+ MB\n",
+      "(21597, 21)\n",
+      "\n",
+      "Index(['id', 'date', 'price', 'bedrooms', 'bathrooms', 'sqft_living',\n",
+      "       'sqft_lot', 'floors', 'waterfront', 'view', 'condition', 'grade',\n",
+      "       'sqft_above', 'sqft_basement', 'yr_built', 'yr_renovated', 'zipcode',\n",
+      "       'lat', 'long', 'sqft_living15', 'sqft_lot15'],\n",
+      "      dtype='object')\n",
+      "\n",
+      "id                 int64\n",
+      "date              object\n",
+      "price            float64\n",
+      "bedrooms           int64\n",
+      "bathrooms        float64\n",
+      "sqft_living        int64\n",
+      "sqft_lot           int64\n",
+      "floors           float64\n",
+      "waterfront        object\n",
+      "view              object\n",
+      "condition         object\n",
+      "grade             object\n",
+      "sqft_above         int64\n",
+      "sqft_basement     object\n",
+      "yr_built           int64\n",
+      "yr_renovated     float64\n",
+      "zipcode            int64\n",
+      "lat              float64\n",
+      "long             float64\n",
+      "sqft_living15      int64\n",
+      "sqft_lot15         int64\n",
+      "dtype: object\n",
+      "\n",
+      "None\n",
+      "\n",
+      "                 id         price      bedrooms     bathrooms   sqft_living  \\\n",
+      "count  2.159700e+04  2.159700e+04  21597.000000  21597.000000  21597.000000   \n",
+      "mean   4.580474e+09  5.402966e+05      3.373200      2.115826   2080.321850   \n",
+      "std    2.876736e+09  3.673681e+05      0.926299      0.768984    918.106125   \n",
+      "min    1.000102e+06  7.800000e+04      1.000000      0.500000    370.000000   \n",
+      "25%    2.123049e+09  3.220000e+05      3.000000      1.750000   1430.000000   \n",
+      "50%    3.904930e+09  4.500000e+05      3.000000      2.250000   1910.000000   \n",
+      "75%    7.308900e+09  6.450000e+05      4.000000      2.500000   2550.000000   \n",
+      "max    9.900000e+09  7.700000e+06     33.000000      8.000000  13540.000000   \n",
+      "\n",
+      "           sqft_lot        floors    sqft_above      yr_built  yr_renovated  \\\n",
+      "count  2.159700e+04  21597.000000  21597.000000  21597.000000  17755.000000   \n",
+      "mean   1.509941e+04      1.494096   1788.596842   1970.999676     83.636778   \n",
+      "std    4.141264e+04      0.539683    827.759761     29.375234    399.946414   \n",
+      "min    5.200000e+02      1.000000    370.000000   1900.000000      0.000000   \n",
+      "25%    5.040000e+03      1.000000   1190.000000   1951.000000      0.000000   \n",
+      "50%    7.618000e+03      1.500000   1560.000000   1975.000000      0.000000   \n",
+      "75%    1.068500e+04      2.000000   2210.000000   1997.000000      0.000000   \n",
+      "max    1.651359e+06      3.500000   9410.000000   2015.000000   2015.000000   \n",
+      "\n",
+      "            zipcode           lat          long  sqft_living15     sqft_lot15  \n",
+      "count  21597.000000  21597.000000  21597.000000   21597.000000   21597.000000  \n",
+      "mean   98077.951845     47.560093   -122.213982    1986.620318   12758.283512  \n",
+      "std       53.513072      0.138552      0.140724     685.230472   27274.441950  \n",
+      "min    98001.000000     47.155900   -122.519000     399.000000     651.000000  \n",
+      "25%    98033.000000     47.471100   -122.328000    1490.000000    5100.000000  \n",
+      "50%    98065.000000     47.571800   -122.231000    1840.000000    7620.000000  \n",
+      "75%    98118.000000     47.678000   -122.125000    2360.000000   10083.000000  \n",
+      "max    98199.000000     47.777600   -121.315000    6210.000000  871200.000000  \n"
+     ]
+    }
+   ],
    "source": [
-    "# Your code here - remember to use markdown cells for comments as well!"
+    "\n",
+    "def dataset_info(file_path):\n",
+    "  data = pd.read_csv(file_path)\n",
+    "  print(\"Dataframe Info:\")\n",
+    "  print(data.shape, data.columns, data.dtypes, data.info(), data.describe(), sep=\"\\n\\n\")\n",
+    "\n",
+    "dataset_info('data/kc_house_data.csv')\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Data Buckets**\n",
+    "\n",
+    "* We have 6 categories for descriptive information like location(categorical data).\n",
+    "* There are 12 columns with numerical values like square footage and bedrooms(numeric data).\n",
+    "* Three columns contain details related to time like year built(temporal data).\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**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."
    ]
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "learn-env",
    "language": "python",
    "name": "python3"
   },
@@ -40,7 +471,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.4"
+   "version": "3.8.5"
   }
  },
  "nbformat": 4,