def build_service_data(filename):
    # Loading data directly with polars leads to errors
    # Some rows end up missing for an unknown reason
    # FIX: Load in pandas then convert to polars
    service_data_pd = pd.read_csv(filename)

    # Quick test to assure the unique key is in fact unique
    assert service_data_pd["Unique Key"].nunique() == len(service_data_pd)
    
    # Load from pandas Dataframe
    service_data_pd["Incident Zip"] = service_data_pd["Incident Zip"].astype("string")
    service_data_pd["BBL"] = service_data_pd["BBL"].astype("string")
    service_data = pl.DataFrame(service_data_pd)
    
    # Clear some ram
    del service_data_pd
    gc.collect()

    drop_cols = [
        "Unique Key", "Agency Name", "Location Type", "Incident Zip",
        "Incident Address", "Street Name", "Cross Street 1",
        "Cross Street 2", "Intersection Street 1", "Intersection Street 2",
        "Address Type", "City", "Landmark", "Facility Type",
        "Status", "Due Date", "Resolution Description",
        "Resolution Action Updated Date", "Community Board",
        "BBL", "X Coordinate (State Plane)", "Y Coordinate (State Plane)",
        "Open Data Channel Type", "Park Facility Name", "Park Borough",
        "Vehicle Type", "Taxi Company Borough", "Taxi Pick Up Location",
        "Bridge Highway Name", "Bridge Highway Direction", "Road Ramp",
        "Bridge Highway Segment", "Location", "Created Year"
    ]

    # Drop columns and create the date variable
    service_data = service_data.drop(drop_cols)
    service_data = create_datetime(service_data, "Created Date")
    service_data = create_datetime(service_data, "Closed Date")

    # Group by date to get the number of Created tickets (as target)
    sd_grouped = service_data.rename({"Created Date": "Datetime"}).group_by("Datetime").agg(
        pl.len().alias("Target"),
    ).sort(by="Datetime")

    # Calculate the number of closed tickets
    # Mean diff used to filter service data
    # mean_diff = service_data.with_columns(
    #     diff_created_closed = pl.col("Closed Date") - pl.col("Created Date")
    # ).filter((pl.col("Closed Date").dt.year() >= 2016) & (pl.col("Closed Date").dt.year() < 2020))["diff_created_closed"].mean().days
    # Mean diff precalculated as 
    mean_diff = 13

    # Create new Closed date with errors filled using the mean diff above
    service_data = service_data.with_columns(
        Closed_Date_New = pl.when(pl.col("Created Date") - pl.col("Closed Date")  > pl.duration(days=1))
                                .then(pl.col("Created Date") + pl.duration(days=mean_diff))
                                .otherwise(pl.col("Closed Date")).fill_null(pl.col("Created Date") + pl.duration(days=mean_diff))
    )

    # Filter tickets such that the closed date < the created date to prevent future data leakage in our dataset
    # We want to make sure future data is not accidentally leaked across other points in our data
    closed_tickets = service_data.group_by(["Closed_Date_New", "Created Date"]) \
        .agg((pl.when(pl.col("Created Date") <= pl.col("Closed_Date_New")).then(1).otherwise(0)).sum().alias("count")) \
        .sort("Closed_Date_New") \
        .filter((pl.col("Closed_Date_New").dt.year() >= 2016) & (pl.col("Closed_Date_New").dt.year() < 2019)) \
        .group_by("Closed_Date_New").agg(pl.col("count").sum().alias("num_closed_tickets"))

    # Rename this column to num closed tickets
    ct_df = closed_tickets.with_columns(
        pl.col("num_closed_tickets")
    )

    # Concat the new columns into our data
    sd_df = pl.concat([sd_grouped, ct_df.drop("Closed_Date_New")], how="horizontal")

    assert len(sd_grouped) == len(ct_df)

    # CATEGORICAL FEATURE MAPPING
    # MAPPING FOR BOROUGH
    Borough_Map = {
        "Unspecified": "OTHER",
        "2017": "OTHER",
        None: "OTHER",
        "2016": "OTHER"
    }
    service_data = service_data.with_columns(
        pl.col("Borough").replace(Borough_Map)
    )

    # MAPPING FOR AGENCY
    # This mapping was done Manually
    Agency_Map = {
        "NYPD": "Security", "HPD": "Buildings", "DOT": "Transportation",
        "DSNY": "Environment & Sanitation", "DEP": "Environment & Sanitation",
        "DOB": "Buildings", "DOE": "Buildings", "DPR": "Parks",
        "DOHMH": "Health", "DOF": "Other", "DHS": "Security",
        "TLC": "Transportation", "HRA": "Other", "DCA": "Other",
        "DFTA": "Other", "EDC": "Other", "DOITT": "Other", "OMB": "Other",
        "DCAS": "Other", "NYCEM": "Other", "ACS": "Other", "3-1-1": "Other",
        "TAX": "Other", "DCP": "Other", "DORIS": "Other", "FDNY": "Other",
        "TAT": "Other", "COIB": "Other", "CEO": "Other", "MOC": "Other",
    }
    
    service_data = service_data.with_columns(
        pl.col("Agency").replace(Agency_Map).alias("AG") # AG Shorthand for Agency Groups
    )


    # Mapping for Descriptor using BERTopic
    # Store descriptors as pandas dataframe (polars not supported)
    # Drop any nan values, and we only care about the unique values
    descriptor_docs = service_data["Descriptor"].unique().to_numpy()
    
    # Build our topic mapping using the pretrained BERTopic model
    # Load model and get predictions
    topic_model = BERTopic.load("models/BERTopic")
    topics, probs = topic_model.transform(descriptor_docs)
    
    # Visualize if wanted
    # topic_model.visualize_barchart(list(range(-1,6,1)))
    
    # Create a topic to ID map
    topic_df = topic_model.get_topic_info()
    topic_id_map = {row["Topic"]: row["Name"][2:] for _, row in topic_df.iterrows()}
    topic_id_map[-1] = topic_id_map[-1][1:] # Fix for the -1 topic case
    
    # For each document (descriptor string) get a mapping of topics
    doc_to_topic_map = defaultdict(str)
    for topic_id, doc in zip(topics, descriptor_docs):
        topic = topic_id_map[topic_id]
        doc_to_topic_map[doc] = topic
    
    service_data = service_data.with_columns(
        pl.col("Descriptor").replace(doc_to_topic_map).alias("DG") # DG Shorthand for descriptor Groups
    )
    
    
    # One Hot Encode Features
    cat_features = ["AG", "Borough", "DG"]
    service_data = service_data.to_dummies(columns=cat_features)
    
    
    # Group by Date and create our Category Feature Vector
    cat_df = service_data.rename({"Created Date": "Datetime"}).group_by("Datetime").agg(
        # Categorical Features Sum
        pl.col('^AG_.*$').sum(),
        pl.col('^Borough_.*$').sum(),
        pl.col('^DG_.*$').sum(),
    ).sort(by="Datetime")
    
    # Concat our category features to our current dataframe
    sd_df = pl.concat([sd_df, cat_df.drop("Datetime")], how="horizontal")
    
    # Now that our dataframe is significantly reduced in size
    # We can finally convert back to a pandas dataframe
    # as pandas is usable across more python packages
    sd_df = sd_df.to_pandas()
    
    # Set index to datetime
    sd_df = sd_df.set_index("Datetime")
    
    # NOTE we added 7 new rows to our weather df 
    # These 7 new rows will essentially be our final pred set
    # The Target for these rows will be null -> indicating it needs to be predicted
    # Add these rows to the service dataframe
    preds_df = pd.DataFrame({'Datetime': pd.date_range(start=sd_df.index[-1], periods=8, freq='D')})[1:]
    sd_df = pd.concat([sd_df, preds_df.set_index("Datetime")], axis=0)

    return sd_df