workshop3.ipynb

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    "### Edinburgh School of AI - Workshop 3"
   ]
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   "source": [
    "Ahoa!\n",
    "\n",
    "In this workshop we are going to put into practice everything we have been learning so far in the course. The aim is to make the theory as intutive as possible, therefore we are going to work with a real life problem.\n",
    "\n",
    "Your neighbour has foundout out that you are on the road to becoming a Machine learning expert and she needs some help with her real state business. Precisely she needs some help predicting housing prices for regions in the USA. It would be great if you could somehow create a model for her that allows her to put in a few features of a house and returns back an estimate of the house selling price.\n",
    "\n",
    "\n",
    "Your neighbor provides you with information about several houses in similar regions.\n",
    "\n",
    "The data contains the following columns:\n",
    "\n",
    "* 'Avg. Area Income': Avg. Income of residents of the city house is located in.\n",
    "* 'Avg. Area House Age': Avg Age of Houses in same city\n",
    "* 'Avg. Area Number of Rooms': Avg Number of Rooms for Houses in same city\n",
    "* 'Avg. Area Number of Bedrooms': Avg Number of Bedrooms for Houses in same city\n",
    "* 'Area Population': Population of city house is located in\n",
    "* 'Price': Price that the house sold at\n",
    "* 'Address': Address for the house"
   ]
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   "source": [
    "### STEP 1"
   ]
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   "source": [
    "#### Import Libraries\n",
    "\n",
    "import pandas, numpy, seaborn and matplotlib (use the % matplotlib inline command to display within frontends) "
   ]
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    "#### Import the data using pandas data frame "
   ]
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    "#### Take a look at the first 5 values of the data (print head)"
   ]
  },
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   "source": [
    "#### Find more info about the data (data types, number of cols and rows)"
   ]
  },
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   "source": [
    "#### Find measure of central tendency of your data (use describe function)"
   ]
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   "source": [
    "### STEP 2 Inital exploratpry analysis"
   ]
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   "source": [
    "#### Visulize your data using seaborn's built in function(s) e.g Pairplot "
   ]
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    "#### Describe the relationship between your data points. Does every plot make sence? Why?"
   ]
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    "#### Print the correlation between every feature (you can use seaborn's heatmap function for this)"
   ]
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   "source": [
    "### STEP 3 Train and Validate your model"
   ]
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   "source": [
    "#### Print all colum names and identify the target variable and all the training features"
   ]
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   "source": [
    "### Creat your X and Y arrays"
   ]
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    "#### Split your data into Train Test "
   ]
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   "source": [
    "#### Create a train the model (you can use sklearn for this)"
   ]
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   "source": [
    "#### Evaluate your Model (print the intercept/bias)"
   ]
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    "#### Print the coefficients"
   ]
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    "#### Interpret your coefficients. Does it sound right?"
   ]
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    "#### Make predictions "
   ]
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   "source": [
    "#### Plot your predicted values against actual values "
   ]
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   "source": [
    "#### Check wheather or not the error term (residuals) are normally distributed (you may use seaborn's displot function)"
   ]
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    "### Evaluate Regression Metrices\n",
    "\n",
    "Here are three common evaluation metrics for regression problems:\n",
    "\n",
    "**Mean Absolute Error** (MAE) is the mean of the absolute value of the errors:\n",
    "\n",
    "$$\\frac 1n\\sum_{i=1}^n|y_i-\\hat{y}_i|$$\n",
    "\n",
    "**Mean Squared Error** (MSE) is the mean of the squared errors:\n",
    "\n",
    "$$\\frac 1n\\sum_{i=1}^n(y_i-\\hat{y}_i)^2$$\n",
    "\n",
    "**Root Mean Squared Error** (RMSE) is the square root of the mean of the squared errors:\n",
    "\n",
    "$$\\sqrt{\\frac 1n\\sum_{i=1}^n(y_i-\\hat{y}_i)^2}$$\n",
    "\n",
    "Comparing these metrics:\n",
    "\n",
    "- **MAE** is the easiest to understand, because it's the average error.\n",
    "- **MSE** is more popular than MAE, because MSE \"punishes\" larger errors, which tends to be useful in the real world.\n",
    "- **RMSE** is even more popular than MSE, because RMSE is interpretable in the \"y\" units.\n",
    "\n",
    "All of these are **loss functions**, because we want to minimize them."
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   "source": [
    "#### import metrics from sklearn "
   ]
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    "#### Print MAE, MSE ans RMSE for your model "
   ]
  },
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