Information Preparation & Exploratory Evaluation
Now that we’ve outlined our method, let’s check out our information and what sort of options we’re working with.
From the above, we see our information accommodates ~197,000 deliveries, with a wide range of numeric & non-numeric options. Not one of the options are lacking a big proportion of values (lowest non-null rely ~181,000), so we possible gained’t have to fret about dropping any options solely.
Let’s verify if our information accommodates any duplicated deliveries, and if there are any observations that we can’t compute supply time for.
print(f"Variety of duplicates: {df.duplicated().sum()} n")print(pd.DataFrame({'Lacking Rely': df[['created_at', 'actual_delivery_time']].isna().sum()}))
We see that each one the deliveries are distinctive. Nonetheless, there are 7 deliveries which are lacking a price for actual_delivery_time, which suggests we gained’t be capable of compute the supply period for these orders. Since there’s solely a handful of those, we’ll take away these observations from our information.
Now, let’s create our prediction goal. We need to predict the supply period (in seconds), which is the elapsed time between when the client positioned the order (‘created_at’) and after they recieved the order (‘actual_delivery_time’).
# convert columns to datetime
df['created_at'] = pd.to_datetime(df['created_at'], utc=True)
df['actual_delivery_time'] = pd.to_datetime(df['actual_delivery_time'], utc=True)# create prediction goal
df['seconds_to_delivery'] = (df['actual_delivery_time'] - df['created_at']).dt.total_seconds()
The very last thing we’ll do earlier than splitting our information into prepare/check is verify for lacking values. We already seen the non-null counts for every characteristic above, however let’s view the proportions to get a greater image.
We see that the market options (‘onshift_dashers’, ‘busy_dashers’, ‘outstanding_orders’) have the best proportion of lacking values (~8% lacking). The characteristic with the second-highest lacking information charge is ‘store_primary_category’ (~2%). All different options have < 1% lacking.
Since not one of the options have a excessive lacking rely, we gained’t take away any of them. Afterward, we’ll have a look at the characteristic distributions to assist us resolve methods to appropriately take care of lacking observations for every characteristic.
However first, let’s cut up our information into prepare/check. We’ll proceed with an 80/20 cut up, and we’ll write this check information to a separate file which we gained’t contact till evaluating our closing mannequin.
from sklearn.model_selection import train_test_split
import os# shuffle
df = df.pattern(frac=1, random_state=42)
df = df.reset_index(drop=True)
# cut up
train_df, test_df = train_test_split(df, test_size=0.2, random_state=42)
# write check information to separate file
listing = 'datasets'
file_name = 'test_data.csv'
file_path = os.path.be part of(listing, file_name)
os.makedirs(listing, exist_ok=True)
test_df.to_csv(file_path, index=False)
Now, let’s dive into the specifics of our prepare information. We’ll set up our numeric & categorical options, to make it clear which columns are being referenced in later exploratory steps.
categorical_feats = [
'market_id',
'store_id',
'store_primary_category',
'order_protocol'
]numeric_feats = [
'total_items',
'subtotal',
'num_distinct_items',
'min_item_price',
'max_item_price',
'total_onshift_dashers',
'total_busy_dashers',
'total_outstanding_orders',
'estimated_order_place_duration',
'estimated_store_to_consumer_driving_duration'
]
Let’s revisit the explicit options with lacking values (‘market_id’, ‘store_primary_category’, ‘order_protocol’). Since there was little lacking information amongst these options (< 3%), we’ll merely impute these lacking values with an “unknown” class.
- This fashion, we gained’t must take away information from different options.
- Maybe the absence of characteristic values holds some predictive energy for supply period i.e. these options usually are not lacking at random.
- Moreover, we’ll add this imputation step to our preprocessing pipeline throughout modeling, in order that we gained’t must manually duplicate this work on our check set.
missing_cols_categorical = ['market_id', 'store_primary_category', 'order_protocol']train_df[missing_cols_categorical] = train_df[missing_cols_categorical].fillna("unknown")
Let’s have a look at our categorical options.
pd.DataFrame({'Cardinality': train_df[categorical_feats].nunique()}).rename_axis('Characteristic')
Since ‘market_id’ & ‘order_protocol’ have low cardinality, we are able to visualize their distributions simply. However, ‘store_id’ & ‘store_primary_category’ are excessive cardinality options. We’ll take a deeper have a look at these later.
import seaborn as sns
import matplotlib.pyplot as pltcategorical_feats_subset = [
'market_id',
'order_protocol'
]
# Arrange the grid
fig, axes = plt.subplots(1, len(categorical_feats_subset), figsize=(13, 5), sharey=True)
# Create barplots for every variable
for i, col in enumerate(categorical_feats_subset):
sns.countplot(x=col, information=train_df, ax=axes[i])
axes[i].set_title(f"Frequencies: {col}")
# Alter structure
plt.tight_layout()
plt.present()
Some key issues to notice:
- ~70% of orders positioned have ‘market_id’ of 1, 2, 4
- < 1% of orders have ‘order_protocol’ of 6 or 7
Sadly, we don’t have any extra details about these variables, corresponding to which ‘market_id’ values are related to which cities/places, and what every ‘order_protocol’ quantity represents. At this level, asking for extra information regarding this info could also be a good suggestion, as it could assist for investigating developments in supply period throughout broader area/location categorizations.
Let’s have a look at our increased cardinality categorical options. Maybe every ‘store_primary_category’ has an related ‘store_id’ vary? In that case, we might not want ‘store_id’, as ‘store_primary_category’ would already encapsulate a whole lot of the details about the shop being ordered from.
store_info = train_df[['store_id', 'store_primary_category']]store_info.groupby('store_primary_category')['store_id'].agg(['min', 'max'])
Clearly not the case: we see that ‘store_id’ ranges overlap throughout ranges of ‘store_primary_category’.
A fast have a look at the distinct values and related frequencies for ‘store_id’ & ‘store_primary_category’ reveals that these options have excessive cardinality and are sparsely distributed. On the whole, excessive cardinality categorical options could also be problematic in regression duties, significantly for regression algorithms that require solely numeric information. When these excessive cardinality options are encoded, they could enlarge the characteristic house drastically, making the accessible information sparse and lowering the mannequin’s capability to generalize to new observations in that characteristic house. For a greater & extra skilled clarification of the phenomena, you’ll be able to learn extra about it right here.
Let’s get a way of how sparsely distributed these options are.
store_id_values = train_df['store_id'].value_counts()# Plot the histogram
plt.determine(figsize=(8, 5))
plt.bar(store_id_values.index, store_id_values.values, colour='skyblue')
# Add titles and labels
plt.title('Worth Counts: store_id', fontsize=14)
plt.xlabel('store_id', fontsize=12)
plt.ylabel('Frequency', fontsize=12)
plt.xticks(rotation=45) # Rotate x-axis labels for higher readability
plt.tight_layout()
plt.present()
We see that there are a handful of shops which have a whole lot of orders, however the majority of them have a lot lower than 100.
To deal with the excessive cardinality of ‘store_id’, we’ll create one other characteristic, ‘store_id_freq’, that teams the ‘store_id’ values by frequency.
- We’ll group the ‘store_id’ values into 5 totally different percentile bins proven under.
- ‘store_id_freq’ could have a lot decrease cardinality than ‘store_id’, however will retain related info relating to the recognition of the shop the supply was ordered from.
- For extra inspiration behind this logic, try this thread.
def encode_frequency(freq, percentiles) -> str:
if freq < percentiles[0]:
return '[0-50)'
elif freq < percentiles[1]:
return '[50-75)'
elif freq < percentiles[2]:
return '[75-90)'
elif freq < percentiles[3]:
return '[90-99)'
else:
return '99+'value_counts = train_df['store_id'].value_counts()
percentiles = np.percentile(value_counts, [50, 75, 90, 99])
# apply encode_frequency to every store_id primarily based on their variety of orders
train_df['store_id_freq'] = train_df['store_id'].apply(lambda x: encode_frequency(value_counts[x], percentiles))
pd.DataFrame({'Rely':train_df['store_id_freq'].value_counts()}).rename_axis('Frequency Bin')
Our encoding reveals us that ~60,000 deliveries have been ordered from shops catgorized within the 90–99th percentile when it comes to reputation, whereas ~12,000 deliveries have been ordered from shops that have been within the 0–fiftieth percentile in reputation.
Now that we’ve (tried) to seize related ‘store_id’ info in a decrease dimension, let’s attempt to do one thing comparable with ‘store_primary_category’.
Let’s have a look at the preferred ‘store_primary_category’ ranges.
A fast look reveals us that many of those ‘store_primary_category’ ranges usually are not unique to one another (ex: ‘american’ & ‘burger’). Additional investigation reveals many extra examples of this type of overlap.
So, let’s attempt to map these distinct retailer classes into a number of primary, all-encompassing teams.
store_category_map = {
'american': ['american', 'burger', 'sandwich', 'barbeque'],
'asian': ['asian', 'chinese', 'japanese', 'indian', 'thai', 'vietnamese', 'dim-sum', 'korean',
'sushi', 'bubble-tea', 'malaysian', 'singaporean', 'indonesian', 'russian'],
'mexican': ['mexican'],
'italian': ['italian', 'pizza'],
}def map_to_category_type(class: str) -> str:
for category_type, classes in store_category_map.gadgets():
if class in classes:
return category_type
return "different"
train_df['store_category_type'] = train_df['store_primary_category'].apply(lambda x: map_to_category_type(x))
value_counts = train_df['store_category_type'].value_counts()
# Plot pie chart
plt.determine(figsize=(6, 6))
value_counts.plot.pie(autopct='%1.1f%%', startangle=90, cmap='viridis', labels=value_counts.index)
plt.title('Class Distribution')
plt.ylabel('') # Cover y-axis label for aesthetics
plt.present()
This grouping might be brutally easy, and there might very nicely be a greater technique to group these retailer classes. Let’s proceed with it for now for simplicity.
We’ve achieved a great deal of investigation into our categorical options. Let’s have a look at the distributions for our numeric options.
# Create grid for boxplots
fig, axes = plt.subplots(nrows=5, ncols=2, figsize=(12, 15)) # Alter determine measurement
axes = axes.flatten() # Flatten the 5x2 axes right into a 1D array for simpler iteration# Generate boxplots for every numeric characteristic
for i, column in enumerate(numeric_feats):
sns.boxplot(y=train_df[column], ax=axes[i])
axes[i].set_title(f"Boxplot for {column}")
axes[i].set_ylabel(column)
# Take away any unused subplots (if any)
for i in vary(len(numeric_feats), len(axes)):
fig.delaxes(axes[i])
# Alter structure for higher spacing
plt.tight_layout()
plt.present()
Lots of the distributions seem like extra proper skewed then they’re as a result of presence of outliers.
Particularly, there appears to be an order with 400+ gadgets. This appears unusual as the following largest order is lower than 100 gadgets.
Let’s look extra into that 400+ merchandise order.
train_df[train_df['total_items']==train_df['total_items'].max()]
