Knowledge Preparation & Exploratory Evaluation
Now that we’ve outlined our method, let’s check out our knowledge and what sort of options we’re working with.
From the above, we see our knowledge incorporates ~197,000 deliveries, with quite a lot of numeric & non-numeric options. Not one of the options are lacking a big share of values (lowest non-null depend ~181,000), so we seemingly gained’t have to fret about dropping any options solely.
Let’s examine if our knowledge incorporates 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 might be lacking a worth for actual_delivery_time, which suggests we gained’t be capable of compute the supply length for these orders. Since there’s solely a handful of those, we’ll take away these observations from our knowledge.
Now, let’s create our prediction goal. We need to predict the supply length (in seconds), which is the elapsed time between when the shopper 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 knowledge into practice/check is examine for lacking values. We already considered 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 share of lacking values (~8% lacking). The characteristic with the second-highest lacking knowledge charge is ‘store_primary_category’ (~2%). All different options have < 1% lacking.
Since not one of the options have a excessive lacking depend, we gained’t take away any of them. Afterward, we’ll have a look at the characteristic distributions to assist us determine how you can appropriately take care of lacking observations for every characteristic.
However first, let’s cut up our knowledge into practice/check. We are going to proceed with an 80/20 cut up, and we’ll write this check knowledge to a separate file which we gained’t contact till evaluating our ultimate 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 knowledge to separate file
listing = 'datasets'
file_name = 'test_data.csv'
file_path = os.path.be a 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 practice knowledge. 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 knowledge amongst these options (< 3%), we’ll merely impute these lacking values with an “unknown” class.
- This manner, we gained’t should take away knowledge from different options.
- Maybe the absence of characteristic values holds some predictive energy for supply length i.e. these options usually are not missing at random.
- Moreover, we’ll add this imputation step to our preprocessing pipeline throughout modeling, in order that we gained’t should 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('Function')
Since ‘market_id’ & ‘order_protocol’ have low cardinality, we will visualize their distributions simply. Alternatively, ‘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, knowledge=train_df, ax=axes[i])
axes[i].set_title(f"Frequencies: {col}")
# Modify 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 further details about these variables, similar to which ‘market_id’ values are related to which cities/places, and what every ‘order_protocol’ quantity represents. At this level, asking for extra knowledge regarding this data could also be a good suggestion, as it could assist for investigating traits in supply length throughout broader area/location categorizations.
Let’s have a look at our greater cardinality categorical options. Maybe every ‘store_primary_category’ has an related ‘store_id’ vary? If that’s the case, we might not want ‘store_id’, as ‘store_primary_category’ would already encapsulate plenty 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’ exhibits that these options have high 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 knowledge. When these excessive cardinality options are encoded, they could enlarge the characteristic house drastically, making the obtainable knowledge sparse and reducing the mannequin’s capability to generalize to new observations in that characteristic house. For a greater & extra skilled rationalization of the phenomena, you’ll be able to learn extra about it 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, coloration='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 tons of 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 data 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 exhibits us that ~60,000 deliveries had been ordered from shops catgorized within the 90–99th percentile by way of recognition, whereas ~12,000 deliveries had been ordered from shops that had been within the 0–fiftieth percentile in recognition.
Now that we’ve (tried) to seize related ‘store_id’ data in a decrease dimension, let’s attempt to do one thing comparable with ‘store_primary_category’.
Let’s have a look at the most well-liked ‘store_primary_category’ ranges.
A fast look exhibits us that many of those ‘store_primary_category’ ranges usually are not unique to one another (ex: ‘american’ & ‘burger’). Additional investigation exhibits many extra examples of this sort of overlap.
So, let’s attempt to map these distinct retailer classes into a couple of fundamental, 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('') # Disguise y-axis label for aesthetics
plt.present()
This grouping might be brutally easy, and there might very properly be a greater approach to group these retailer classes. Let’s proceed with it for now for simplicity.
We’ve finished 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)) # Modify determine dimension
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])
# Modify structure for higher spacing
plt.tight_layout()
plt.present()
Lots of the distributions seem like extra proper skewed then they’re because of the presence of outliers.
Specifically, there appears to be an order with 400+ gadgets. This appears unusual as the subsequent 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()]
