Information Preparation & Exploratory Evaluation
Now that we’ve outlined our strategy, let’s check out our information and how much 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 share of values (lowest non-null depend ~181,000), so we probably gained’t have to fret about dropping any options completely.
Let’s examine if our information accommodates any duplicated deliveries, and if there are any observations that we can not 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. Nevertheless, there are 7 deliveries which might be lacking a price for actual_delivery_time, which implies we gained’t be capable to 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 shopper positioned the order (‘created_at’) and once 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 practice/check is examine 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 share 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 depend, we gained’t take away any of them. Afterward, we’ll take a look at the characteristic distributions to assist us resolve how one can appropriately take care of lacking observations for every characteristic.
However first, let’s break up our information into practice/check. We are going to proceed with an 80/20 break up, and we’ll write this check information 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)
# break 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 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 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 need to take away information from different options.
- Maybe the absence of characteristic values holds some predictive energy for supply period i.e. these options will not be missing at random.
- Moreover, we’ll add this imputation step to our preprocessing pipeline throughout modeling, in order that we gained’t need to 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 take 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 are able to visualize their distributions simply. Alternatively, ‘store_id’ & ‘store_primary_category’ are excessive cardinality options. We’ll take a deeper take 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 format
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/areas, and what every ‘order_protocol’ quantity represents. At this level, asking for extra information regarding this data could also be a good suggestion, as it might assist for investigating tendencies in supply period throughout broader area/location categorizations.
Let’s take a look at our larger cardinality categorical options. Maybe every ‘store_primary_category’ has an related ‘store_id’ vary? If that’s the case, we could not want ‘store_id’, as ‘store_primary_category’ would already encapsulate a number 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 take a look at the distinct values and related frequencies for ‘store_id’ & ‘store_primary_category’ reveals that these options have high cardinality and are sparsely distributed. On the whole, excessive cardinality categorical options could also be problematic in regression duties, notably for regression algorithms that require solely numeric information. When these excessive cardinality options are encoded, they could enlarge the characteristic house drastically, making the obtainable information sparse and lowering the mannequin’s means to generalize to new observations in that characteristic house. For a greater & extra skilled clarification of the phenomena, you possibly can 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, 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 lots 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’ may have a lot decrease cardinality than ‘store_id’, however will retain related data concerning 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 had been ordered from shops catgorized within the 90–99th percentile by way of reputation, whereas ~12,000 deliveries had been ordered from shops that had been within the 0–fiftieth percentile in reputation.
Now that we’ve (tried) to seize related ‘store_id’ data in a decrease dimension, let’s attempt to do one thing related with ‘store_primary_category’.
Let’s take a look at the preferred ‘store_primary_category’ ranges.
A fast look reveals us that many of those ‘store_primary_category’ ranges will not be unique to one another (ex: ‘american’ & ‘burger’). Additional investigation reveals many extra examples of this sort of overlap.
So, let’s attempt to map these distinct retailer classes into a couple 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.objects():
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('') # Conceal y-axis label for aesthetics
plt.present()
This grouping might be brutally easy, and there could very properly be a greater option to group these retailer classes. Let’s proceed with it for now for simplicity.
We’ve performed a great deal of investigation into our categorical options. Let’s take 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 format for higher spacing
plt.tight_layout()
plt.present()
Most 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+ objects. This appears unusual as the following largest order is lower than 100 objects.
Let’s look extra into that 400+ merchandise order.
train_df[train_df['total_items']==train_df['total_items'].max()]
