Project 2: Predicting House Prices
Data Collection and Preprocessing
Now that we've defined our problem statement, we can't wait to dive into the data, can we? Data is the bedrock of any machine learning project. It's like the paint for an artist—without it, there's no masterpiece. But remember, a messy palette won't create a Mona Lisa! Similarly, messy data won't help us build a reliable model. So, it's crucial to understand and preprocess our data before we move on to the fun part—modeling!
Data Collection
For this project, we'll assume you've got your hands on a rich dataset that contains various features of houses, along with their selling prices. This could be a publicly available dataset or one you've gathered yourself.
Example Code: Exploring the Dataset
Before we go any further, let's take a look at the dataset's features and a few sample entries to get a better understanding.
# Viewing the columns in the dataset
print("Columns in the dataset: ", df.columns)
# Summary statistics
print("\\nSummary statistics:")
print(df.describe())Data Preprocessing
Data preprocessing is like housekeeping for data scientists. It might not be the most exciting part of the job, but it's absolutely vital.
Handling Missing Values
Missing values can distort the predictive power of a model. So, let's find out if we have any.
# Checking for missing values
missing_values = df.isnull().sum()
print("Missing values per column:")
print(missing_values)If any columns have missing values, you could decide to fill them with the mean or median of that column or even decide to remove those rows entirely.
# Filling missing values with the median value of the column
df.fillna(df.median(), inplace=True)Data Encoding
Our dataset might contain categorical variables like 'Neighborhood' or 'Type of Roof'. We need to convert these into numerical values.
# One-hot encoding of categorical variables
df = pd.get_dummies(df, drop_first=True)Feature Scaling
Finally, we need to scale our features so that no variable has more influence than another.
from sklearn.preprocessing import StandardScaler
scaler = StandardScaler()
df_scaled = scaler.fit_transform(df)And voila, your data is now ready to be fed into a machine learning model!
In the next section, we'll take this preprocessed data and use it to train our predictive models. But for now, give yourself a pat on the back. You've done some quality data housekeeping, and trust us, your future self will thank you!
Stay tuned, and let's keep this learning journey rolling!
Data Collection and Preprocessing
Now that we've defined our problem statement, we can't wait to dive into the data, can we? Data is the bedrock of any machine learning project. It's like the paint for an artist—without it, there's no masterpiece. But remember, a messy palette won't create a Mona Lisa! Similarly, messy data won't help us build a reliable model. So, it's crucial to understand and preprocess our data before we move on to the fun part—modeling!
Data Collection
For this project, we'll assume you've got your hands on a rich dataset that contains various features of houses, along with their selling prices. This could be a publicly available dataset or one you've gathered yourself.
Example Code: Exploring the Dataset
Before we go any further, let's take a look at the dataset's features and a few sample entries to get a better understanding.
# Viewing the columns in the dataset
print("Columns in the dataset: ", df.columns)
# Summary statistics
print("\\nSummary statistics:")
print(df.describe())Data Preprocessing
Data preprocessing is like housekeeping for data scientists. It might not be the most exciting part of the job, but it's absolutely vital.
Handling Missing Values
Missing values can distort the predictive power of a model. So, let's find out if we have any.
# Checking for missing values
missing_values = df.isnull().sum()
print("Missing values per column:")
print(missing_values)If any columns have missing values, you could decide to fill them with the mean or median of that column or even decide to remove those rows entirely.
# Filling missing values with the median value of the column
df.fillna(df.median(), inplace=True)Data Encoding
Our dataset might contain categorical variables like 'Neighborhood' or 'Type of Roof'. We need to convert these into numerical values.
# One-hot encoding of categorical variables
df = pd.get_dummies(df, drop_first=True)Feature Scaling
Finally, we need to scale our features so that no variable has more influence than another.
from sklearn.preprocessing import StandardScaler
scaler = StandardScaler()
df_scaled = scaler.fit_transform(df)And voila, your data is now ready to be fed into a machine learning model!
In the next section, we'll take this preprocessed data and use it to train our predictive models. But for now, give yourself a pat on the back. You've done some quality data housekeeping, and trust us, your future self will thank you!
Stay tuned, and let's keep this learning journey rolling!
Data Collection and Preprocessing
Now that we've defined our problem statement, we can't wait to dive into the data, can we? Data is the bedrock of any machine learning project. It's like the paint for an artist—without it, there's no masterpiece. But remember, a messy palette won't create a Mona Lisa! Similarly, messy data won't help us build a reliable model. So, it's crucial to understand and preprocess our data before we move on to the fun part—modeling!
Data Collection
For this project, we'll assume you've got your hands on a rich dataset that contains various features of houses, along with their selling prices. This could be a publicly available dataset or one you've gathered yourself.
Example Code: Exploring the Dataset
Before we go any further, let's take a look at the dataset's features and a few sample entries to get a better understanding.
# Viewing the columns in the dataset
print("Columns in the dataset: ", df.columns)
# Summary statistics
print("\\nSummary statistics:")
print(df.describe())Data Preprocessing
Data preprocessing is like housekeeping for data scientists. It might not be the most exciting part of the job, but it's absolutely vital.
Handling Missing Values
Missing values can distort the predictive power of a model. So, let's find out if we have any.
# Checking for missing values
missing_values = df.isnull().sum()
print("Missing values per column:")
print(missing_values)If any columns have missing values, you could decide to fill them with the mean or median of that column or even decide to remove those rows entirely.
# Filling missing values with the median value of the column
df.fillna(df.median(), inplace=True)Data Encoding
Our dataset might contain categorical variables like 'Neighborhood' or 'Type of Roof'. We need to convert these into numerical values.
# One-hot encoding of categorical variables
df = pd.get_dummies(df, drop_first=True)Feature Scaling
Finally, we need to scale our features so that no variable has more influence than another.
from sklearn.preprocessing import StandardScaler
scaler = StandardScaler()
df_scaled = scaler.fit_transform(df)And voila, your data is now ready to be fed into a machine learning model!
In the next section, we'll take this preprocessed data and use it to train our predictive models. But for now, give yourself a pat on the back. You've done some quality data housekeeping, and trust us, your future self will thank you!
Stay tuned, and let's keep this learning journey rolling!
Data Collection and Preprocessing
Now that we've defined our problem statement, we can't wait to dive into the data, can we? Data is the bedrock of any machine learning project. It's like the paint for an artist—without it, there's no masterpiece. But remember, a messy palette won't create a Mona Lisa! Similarly, messy data won't help us build a reliable model. So, it's crucial to understand and preprocess our data before we move on to the fun part—modeling!
Data Collection
For this project, we'll assume you've got your hands on a rich dataset that contains various features of houses, along with their selling prices. This could be a publicly available dataset or one you've gathered yourself.
Example Code: Exploring the Dataset
Before we go any further, let's take a look at the dataset's features and a few sample entries to get a better understanding.
# Viewing the columns in the dataset
print("Columns in the dataset: ", df.columns)
# Summary statistics
print("\\nSummary statistics:")
print(df.describe())Data Preprocessing
Data preprocessing is like housekeeping for data scientists. It might not be the most exciting part of the job, but it's absolutely vital.
Handling Missing Values
Missing values can distort the predictive power of a model. So, let's find out if we have any.
# Checking for missing values
missing_values = df.isnull().sum()
print("Missing values per column:")
print(missing_values)If any columns have missing values, you could decide to fill them with the mean or median of that column or even decide to remove those rows entirely.
# Filling missing values with the median value of the column
df.fillna(df.median(), inplace=True)Data Encoding
Our dataset might contain categorical variables like 'Neighborhood' or 'Type of Roof'. We need to convert these into numerical values.
# One-hot encoding of categorical variables
df = pd.get_dummies(df, drop_first=True)Feature Scaling
Finally, we need to scale our features so that no variable has more influence than another.
from sklearn.preprocessing import StandardScaler
scaler = StandardScaler()
df_scaled = scaler.fit_transform(df)And voila, your data is now ready to be fed into a machine learning model!
In the next section, we'll take this preprocessed data and use it to train our predictive models. But for now, give yourself a pat on the back. You've done some quality data housekeeping, and trust us, your future self will thank you!
Stay tuned, and let's keep this learning journey rolling!