Wrote the code for the thesis, Jupiter laptop knocks out the attribute error in the penultimate part

# Keep only the data for the last year for all companies. We shall be taking only latest data into consideration.
nyse_data.drop_duplicates(subset='ticker_symbol', keep='last', inplace=True)
Svm_model = {}
Svm_model.fit()
# Adding in the predicted values for bankruptcy in the original dataset
nyse_data["stability"] = Svm_model.fit(scaler.transform(nyse_data[["total_assets", "total_liabilities"]]))
print ("Companies predicted to go bankrupt over a 4 year period: ", len(nyse_data.loc[nyse_data["stability"] != 1, "ticker_symbol"]))

AttributeError Traceback (most recent call last)
in
2 nyse_data.drop_duplicates(subset='ticker_symbol', keep='last', inplace=True)
3 Svm_model = {}
----> 4 Svm_model.fit()
5 # Adding in the predicted values for bankruptcy in the original dataset
6 nyse_data["stability"] = Svm_model.fit(scaler.transform(nyse_data[["total_assets", "total_liabilities"]]))

AttributeError: 'dict' object has no attribute 'fit'

count = 0
stock_predictions = {}
stats.normaltest = {}
for i in weekly_stock_prices_log:
# Splitting available data into training for accuracy check using remaining data points
split_point = len(weekly_stock_prices_log[i]) - 20
# Number of weeks from last date in dataset to 2018-12-31 = 117
num_of_predictions = len(weekly_stock_prices_log[i]) + 117
training = weekly_stock_prices_log[i][0:split_point]
model = {}

# Try modelling first using p=2, q=1, if that fails use p=1, q=0
try:
model = arima_model.ARMA(training["close"], order=(2,1)).fit()
except:
model = arima_model.ARMA(training["close"], order=(1,0)).fit()

#Add the predicted values in a dataframe for ease of further operations.
daterange = pd.date_range(training.index[0], periods=num_of_predictions, freq = 'W-MON').tolist()
stock_predictions[i] = pd.DataFrame(columns=["date", "prediction"])
stock_predictions[i]["date"] = daterange
stock_predictions[i]["prediction"] = model.predict(start=0, end=num_of_predictions)
stock_predictions[i].set_index("date", inplace=True)

# Draw a QQPlot to check if the residuals are evenly distributed
if count < 5:
resid = model.resid
print("For "+i+": ",stats.normaltest(resid))
qqplot(resid, line='q', fit=True)
plt.show()
count += 1

AttributeError Traceback (most recent call last)
in
1 count = 0
2 stock_predictions = {}
----> 3 stats.normaltest = {}
4 for i in weekly_stock_prices_log:
5 # Splitting available data into training for accuracy check using remaining data points

AttributeError: 'dict' object has no attribute 'normaltest'